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Hire remote Amazon Virtual Assistant

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No exact match for this specialty yet — here are related experts from our network.

Aditya P.
Available Now
Verified in SoftDoesAditya P.
DevOps Engineer/ Site Reliability Engineer
US 🇺🇸English (B2)Senior
GoPythonUNIX Shell ScriptingPHP

Principal DevOps / Site Reliability Engineer with 14+ years of software engineering experience (including freelance development) and 9+ years of professional DevOps/SRE experience. Expert in designing and automating scalable cloud infrastructure across AWS, GCP, Azure, a company Cloud, with deep expertise in Kubernetes, Docker, OpenShift, Terraform, Ansible, GitOps (ArgoCD/FluxCD), CI/CD, Go, and Python. Experienced in building multi-cloud, high-availability platforms, infrastructure automation, cloud migrations, and developer platforms. Currently working as Principal Engineer at FOX, previously held senior engineering roles at Hippo Insurance, a company, and Morgan Stanley.

Aditya P.
Available Now
Aditya P.Verified in SoftDoes
DevOps Engineer/ Site Reliability Engineer
US 🇺🇸English (B2)Senior
GoPythonUNIX Shell ScriptingPHP

Principal DevOps / Site Reliability Engineer with 14+ years of software engineering experience (including freelance development) and 9+ years of professional DevOps/SRE experience. Expert in designing and automating scalable cloud infrastructure across AWS, GCP, Azure, a company Cloud, with deep expertise in Kubernetes, Docker, OpenShift, Terraform, Ansible, GitOps (ArgoCD/FluxCD), CI/CD, Go, and Python. Experienced in building multi-cloud, high-availability platforms, infrastructure automation, cloud migrations, and developer platforms. Currently working as Principal Engineer at FOX, previously held senior engineering roles at Hippo Insurance, a company, and Morgan Stanley.

Aditya P.
Available Now
Aditya P.Verified in SoftDoes
DevOps Engineer/ Site Reliability Engineer
US 🇺🇸English (B2)Senior
GoPythonUNIX Shell ScriptingPHP

Principal DevOps / Site Reliability Engineer with 14+ years of software engineering experience (including freelance development) and 9+ years of professional DevOps/SRE experience. Expert in designing and automating scalable cloud infrastructure across AWS, GCP, Azure, a company Cloud, with deep expertise in Kubernetes, Docker, OpenShift, Terraform, Ansible, GitOps (ArgoCD/FluxCD), CI/CD, Go, and Python. Experienced in building multi-cloud, high-availability platforms, infrastructure automation, cloud migrations, and developer platforms. Currently working as Principal Engineer at FOX, previously held senior engineering roles at Hippo Insurance, a company, and Morgan Stanley.

Andrea M.
Available Now
Verified in SoftDoesAndrea M.
AI Solutions Architect/Engineer
IT 🇮🇹English (C1)Senior
PythonLangChainLangGraphGoogle Cloud

I am a solutions architect and engineer with 5+ years of experience in designing and implementing cloud-based AI solutions. Solid skills across applied AI, software engineering, cloud architecture and DevOps practices, built on years of field experience, allow me to drive AI initiatives from concept to production, leveraging AI tools to boost delivery speed without compromising on deliverable quality.

Andrea M.
Available Now
Andrea M.Verified in SoftDoes
AI Solutions Architect/Engineer
IT 🇮🇹English (C1)Senior
PythonLangChainLangGraphGoogle Cloud

I am a solutions architect and engineer with 5+ years of experience in designing and implementing cloud-based AI solutions. Solid skills across applied AI, software engineering, cloud architecture and DevOps practices, built on years of field experience, allow me to drive AI initiatives from concept to production, leveraging AI tools to boost delivery speed without compromising on deliverable quality.

Andrea M.
Available Now
Andrea M.Verified in SoftDoes
AI Solutions Architect/Engineer
IT 🇮🇹English (C1)Senior
PythonLangChainLangGraphGoogle Cloud

I am a solutions architect and engineer with 5+ years of experience in designing and implementing cloud-based AI solutions. Solid skills across applied AI, software engineering, cloud architecture and DevOps practices, built on years of field experience, allow me to drive AI initiatives from concept to production, leveraging AI tools to boost delivery speed without compromising on deliverable quality.

Andrii V.
Available Now
Verified in SoftDoesAndrii V.
Senior Python Engineer
CA 🇨🇦English (C1)Senior
PythonDjangoFastAPIFlask

Senior Python Developer with 11+ years of experience designing scalable backend systems and microservice architectures across data analytics, logistics, and fintech platforms. Proven track record leading engineering teams and delivering cloud-native solutions on AWS and Kubernetes. Recently led development of AI-powered and real-time observability platforms using modern Python frameworks and large-scale data technologies. Strong background in system architecture, distributed systems, and mentoring teams in Agile environments.

Andrii V.
Available Now
Andrii V.Verified in SoftDoes
Senior Python Engineer
CA 🇨🇦English (C1)Senior
PythonDjangoFastAPIFlask

Senior Python Developer with 11+ years of experience designing scalable backend systems and microservice architectures across data analytics, logistics, and fintech platforms. Proven track record leading engineering teams and delivering cloud-native solutions on AWS and Kubernetes. Recently led development of AI-powered and real-time observability platforms using modern Python frameworks and large-scale data technologies. Strong background in system architecture, distributed systems, and mentoring teams in Agile environments.

Andrii V.
Available Now
Andrii V.Verified in SoftDoes
Senior Python Engineer
CA 🇨🇦English (C1)Senior
PythonDjangoFastAPIFlask

Senior Python Developer with 11+ years of experience designing scalable backend systems and microservice architectures across data analytics, logistics, and fintech platforms. Proven track record leading engineering teams and delivering cloud-native solutions on AWS and Kubernetes. Recently led development of AI-powered and real-time observability platforms using modern Python frameworks and large-scale data technologies. Strong background in system architecture, distributed systems, and mentoring teams in Agile environments.

Boris S.
Available Now
Verified in SoftDoesBoris S.
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BG 🇧🇬English (B2)Senior
PythonSQLBashFastAPI

Senior AI/ML Engineer with 10+ years of experience designing and deploying large-scale machine learning and generative AI systems in production environments. Specialized in LLM architectures, agentic AI systems, distributed ML infrastructure, and end-to-end MLOps platforms. Proven track record of delivering high-impact AI products including diagnostic AI systems improving accuracy by 38% and enterprise ML platforms accelerating data pipelines by 40%. Experienced in leading cross-functional teams, architecting scalable AI platforms, and driving AI innovation across healthcare, enterprise analytics, and automation domains.

Boris S.
Available Now
Boris S.Verified in SoftDoes
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BG 🇧🇬English (B2)Senior
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Senior AI/ML Engineer with 10+ years of experience designing and deploying large-scale machine learning and generative AI systems in production environments. Specialized in LLM architectures, agentic AI systems, distributed ML infrastructure, and end-to-end MLOps platforms. Proven track record of delivering high-impact AI products including diagnostic AI systems improving accuracy by 38% and enterprise ML platforms accelerating data pipelines by 40%. Experienced in leading cross-functional teams, architecting scalable AI platforms, and driving AI innovation across healthcare, enterprise analytics, and automation domains.

Boris S.
Available Now
Boris S.Verified in SoftDoes
Senior AI/ML Engineer
BG 🇧🇬English (B2)Senior
PythonSQLBashFastAPI

Senior AI/ML Engineer with 10+ years of experience designing and deploying large-scale machine learning and generative AI systems in production environments. Specialized in LLM architectures, agentic AI systems, distributed ML infrastructure, and end-to-end MLOps platforms. Proven track record of delivering high-impact AI products including diagnostic AI systems improving accuracy by 38% and enterprise ML platforms accelerating data pipelines by 40%. Experienced in leading cross-functional teams, architecting scalable AI platforms, and driving AI innovation across healthcare, enterprise analytics, and automation domains.

Eugene M.
Available Now
Verified in SoftDoesEugene M.
DevOps Engineer
ES 🇪🇸English (C2)Senior
AWSGoogle CloudKubernetesTerraform

10+ years in IT, 7+ years focused on DevOps/SysOps/SRE. Currently - Tech Lead at a U.S. company.

Eugene M.
Available Now
Eugene M.Verified in SoftDoes
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ES 🇪🇸English (C2)Senior
AWSGoogle CloudKubernetesTerraform

10+ years in IT, 7+ years focused on DevOps/SysOps/SRE. Currently - Tech Lead at a U.S. company.

Eugene M.
Available Now
Eugene M.Verified in SoftDoes
DevOps Engineer
ES 🇪🇸English (C2)Senior
AWSGoogle CloudKubernetesTerraform

10+ years in IT, 7+ years focused on DevOps/SysOps/SRE. Currently - Tech Lead at a U.S. company.

Hripsime S.
Available Now
Verified in SoftDoesHripsime S.
AI Engineer | Data Scientist
FR 🇫🇷English (C1)Senior
Deep LearningMachine LearningInterpretabilityConvolutional Neural Networks

AI engineer with 6+ years of professional experience, available to start a new challenging role in stimulating and innovative company.

Hripsime S.
Available Now
Hripsime S.Verified in SoftDoes
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FR 🇫🇷English (C1)Senior
Deep LearningMachine LearningInterpretabilityConvolutional Neural Networks

AI engineer with 6+ years of professional experience, available to start a new challenging role in stimulating and innovative company.

Hripsime S.
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Hripsime S.Verified in SoftDoes
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FR 🇫🇷English (C1)Senior
Deep LearningMachine LearningInterpretabilityConvolutional Neural Networks

AI engineer with 6+ years of professional experience, available to start a new challenging role in stimulating and innovative company.

Mario J.
Available Now
Verified in SoftDoesMario J.
Senior Data Engineer
GT 🇬🇹English (C1)Senior
PythonSQLPandasPySpark

Over the past 8 years, I have gained ample experience in ETL/ELT, Snowflake, Airflow, and Data Warehouse. I have been responsible for Data Lake Support and creating new ETL/ELT processes within AWS using either Snowflake, Glue, Airflow, dbt, Xplenty, Docker, or Custom Python Scripts with APIs. I have also worked as an Application Architect, which includes over 50 different services across AWS. I needed to create pipelines, organizations, and permissions within AWS Infrastructure to meet business requirements while optimizing costs. My previous work includes over four years as a Data Analyst, creating and supporting BI Tools like Sisense, Quicksight, and Power BI/Tableau. I managed and created dashboards, widgets, and Data Architecture with Sisense and Redshift. I was also the Support Engineer for Sisense Installation and Maintenance. I would describe myself as reliable and able to undertake complex situations with unique solutions. I have over 7 years of experience in the Data sector, over 17+ years of SQL experience, and 20+ years as a Developer. I’m also a mid-QA Automation Engineer, which gives me a better idea of the attention to detail a developer needs to attain to complete development within the time limit and without affecting PROD releases. My other skills include being a Sysadmin in both Windows and Linux environments, a company Web Services AWS, a company 365 Portal, JumpCloud, 1Password, and Slack. I also have DBA knowledge, including creating servers and Data Warehouses. I'm an expert in MS Office and can automate with macros. I’m also a Cybersecurity advocate since I’m always looking for ways to protect data and user permissions. I have been in Agile Development with Jira in Kanban and Scrum for the past 4 years. And I have also created bots to automatize process alerts and configurations with Slack APIs and AWS to help my teammates get prompt feedback from the ETL pipelines.

Mario J.
Available Now
Mario J.Verified in SoftDoes
Senior Data Engineer
GT 🇬🇹English (C1)Senior
PythonSQLPandasPySpark

Over the past 8 years, I have gained ample experience in ETL/ELT, Snowflake, Airflow, and Data Warehouse. I have been responsible for Data Lake Support and creating new ETL/ELT processes within AWS using either Snowflake, Glue, Airflow, dbt, Xplenty, Docker, or Custom Python Scripts with APIs. I have also worked as an Application Architect, which includes over 50 different services across AWS. I needed to create pipelines, organizations, and permissions within AWS Infrastructure to meet business requirements while optimizing costs. My previous work includes over four years as a Data Analyst, creating and supporting BI Tools like Sisense, Quicksight, and Power BI/Tableau. I managed and created dashboards, widgets, and Data Architecture with Sisense and Redshift. I was also the Support Engineer for Sisense Installation and Maintenance. I would describe myself as reliable and able to undertake complex situations with unique solutions. I have over 7 years of experience in the Data sector, over 17+ years of SQL experience, and 20+ years as a Developer. I’m also a mid-QA Automation Engineer, which gives me a better idea of the attention to detail a developer needs to attain to complete development within the time limit and without affecting PROD releases. My other skills include being a Sysadmin in both Windows and Linux environments, a company Web Services AWS, a company 365 Portal, JumpCloud, 1Password, and Slack. I also have DBA knowledge, including creating servers and Data Warehouses. I'm an expert in MS Office and can automate with macros. I’m also a Cybersecurity advocate since I’m always looking for ways to protect data and user permissions. I have been in Agile Development with Jira in Kanban and Scrum for the past 4 years. And I have also created bots to automatize process alerts and configurations with Slack APIs and AWS to help my teammates get prompt feedback from the ETL pipelines.

Mario J.
Available Now
Mario J.Verified in SoftDoes
Senior Data Engineer
GT 🇬🇹English (C1)Senior
PythonSQLPandasPySpark

Over the past 8 years, I have gained ample experience in ETL/ELT, Snowflake, Airflow, and Data Warehouse. I have been responsible for Data Lake Support and creating new ETL/ELT processes within AWS using either Snowflake, Glue, Airflow, dbt, Xplenty, Docker, or Custom Python Scripts with APIs. I have also worked as an Application Architect, which includes over 50 different services across AWS. I needed to create pipelines, organizations, and permissions within AWS Infrastructure to meet business requirements while optimizing costs. My previous work includes over four years as a Data Analyst, creating and supporting BI Tools like Sisense, Quicksight, and Power BI/Tableau. I managed and created dashboards, widgets, and Data Architecture with Sisense and Redshift. I was also the Support Engineer for Sisense Installation and Maintenance. I would describe myself as reliable and able to undertake complex situations with unique solutions. I have over 7 years of experience in the Data sector, over 17+ years of SQL experience, and 20+ years as a Developer. I’m also a mid-QA Automation Engineer, which gives me a better idea of the attention to detail a developer needs to attain to complete development within the time limit and without affecting PROD releases. My other skills include being a Sysadmin in both Windows and Linux environments, a company Web Services AWS, a company 365 Portal, JumpCloud, 1Password, and Slack. I also have DBA knowledge, including creating servers and Data Warehouses. I'm an expert in MS Office and can automate with macros. I’m also a Cybersecurity advocate since I’m always looking for ways to protect data and user permissions. I have been in Agile Development with Jira in Kanban and Scrum for the past 4 years. And I have also created bots to automatize process alerts and configurations with Slack APIs and AWS to help my teammates get prompt feedback from the ETL pipelines.

Raphael O.
Available Now
Verified in SoftDoesRaphael O.
Snr. Staff Software Engineer
US 🇺🇸English (Native)Senior
LeadershipAgileBig dataAPI design

I am a technical leader and problem solver who designs and delivers scalable, distributed software and AI systems that create measurable business value. I have led cross-functional teams and platform initiatives, translating complex requirements into reliable, production-ready solutions built on open standards. My experience spans architecting, mentoring, and driving execution across diverse technology stacks, with a strong focus on operational excellence, product impact, and long-term system sustainability.

Raphael O.
Available Now
Raphael O.Verified in SoftDoes
Snr. Staff Software Engineer
US 🇺🇸English (Native)Senior
LeadershipAgileBig dataAPI design

I am a technical leader and problem solver who designs and delivers scalable, distributed software and AI systems that create measurable business value. I have led cross-functional teams and platform initiatives, translating complex requirements into reliable, production-ready solutions built on open standards. My experience spans architecting, mentoring, and driving execution across diverse technology stacks, with a strong focus on operational excellence, product impact, and long-term system sustainability.

Raphael O.
Available Now
Raphael O.Verified in SoftDoes
Snr. Staff Software Engineer
US 🇺🇸English (Native)Senior
LeadershipAgileBig dataAPI design

I am a technical leader and problem solver who designs and delivers scalable, distributed software and AI systems that create measurable business value. I have led cross-functional teams and platform initiatives, translating complex requirements into reliable, production-ready solutions built on open standards. My experience spans architecting, mentoring, and driving execution across diverse technology stacks, with a strong focus on operational excellence, product impact, and long-term system sustainability.

Santiago G.
Available Now
Verified in SoftDoesSantiago G.
DevOps Engineer
US 🇺🇸English (C1)Senior
LinuxBashDockerAnsible

Energetic, adaptable, mission-focused and multilingual MBA professional with more than 20 years of experience in IT, Business Intelligence, Marketing and Finance in 4 multinationals. Proven track record of finding creative solutions to solving challenges. Effective presenter who collaborates well in teams and across divisions. Passion for learning and acquiring new skills every day.

Santiago G.
Available Now
Santiago G.Verified in SoftDoes
DevOps Engineer
US 🇺🇸English (C1)Senior
LinuxBashDockerAnsible

Energetic, adaptable, mission-focused and multilingual MBA professional with more than 20 years of experience in IT, Business Intelligence, Marketing and Finance in 4 multinationals. Proven track record of finding creative solutions to solving challenges. Effective presenter who collaborates well in teams and across divisions. Passion for learning and acquiring new skills every day.

Santiago G.
Available Now
Santiago G.Verified in SoftDoes
DevOps Engineer
US 🇺🇸English (C1)Senior
LinuxBashDockerAnsible

Energetic, adaptable, mission-focused and multilingual MBA professional with more than 20 years of experience in IT, Business Intelligence, Marketing and Finance in 4 multinationals. Proven track record of finding creative solutions to solving challenges. Effective presenter who collaborates well in teams and across divisions. Passion for learning and acquiring new skills every day.

Thierry M.
Available Now
Verified in SoftDoesThierry M.
Senior Infrastructure / Production Engineer
US 🇺🇸English (C1)Senior
AWSGoogle CloudAzureKubernetes

15+ years of experience designing and operating large-scale distributed systems and latency-sensitive production platforms. Expertise in multi-region Kubernetes infrastructure, hybrid cloud environments, and production reliability engineering, with a strong focus on scalability, fault tolerance, observability, and operational automation. Deep experience building and operating hybrid bare-metal and cloud platforms (AWS, GCP), including Kubernetes infrastructure, traffic routing, CI/CD systems, and production observability stacks supporting globally distributed workloads. Strong background in incident response, Linux systems, networking, distributed systems troubleshooting, and infrastructure automation using Terraform and Python. Focused on end-to-end ownership of production infrastructure platforms while partnering closely with engineering teams to improve reliability, security, deployment consistency, and operational efficiency.

Thierry M.
Available Now
Thierry M.Verified in SoftDoes
Senior Infrastructure / Production Engineer
US 🇺🇸English (C1)Senior
AWSGoogle CloudAzureKubernetes

15+ years of experience designing and operating large-scale distributed systems and latency-sensitive production platforms. Expertise in multi-region Kubernetes infrastructure, hybrid cloud environments, and production reliability engineering, with a strong focus on scalability, fault tolerance, observability, and operational automation. Deep experience building and operating hybrid bare-metal and cloud platforms (AWS, GCP), including Kubernetes infrastructure, traffic routing, CI/CD systems, and production observability stacks supporting globally distributed workloads. Strong background in incident response, Linux systems, networking, distributed systems troubleshooting, and infrastructure automation using Terraform and Python. Focused on end-to-end ownership of production infrastructure platforms while partnering closely with engineering teams to improve reliability, security, deployment consistency, and operational efficiency.

Thierry M.
Available Now
Thierry M.Verified in SoftDoes
Senior Infrastructure / Production Engineer
US 🇺🇸English (C1)Senior
AWSGoogle CloudAzureKubernetes

15+ years of experience designing and operating large-scale distributed systems and latency-sensitive production platforms. Expertise in multi-region Kubernetes infrastructure, hybrid cloud environments, and production reliability engineering, with a strong focus on scalability, fault tolerance, observability, and operational automation. Deep experience building and operating hybrid bare-metal and cloud platforms (AWS, GCP), including Kubernetes infrastructure, traffic routing, CI/CD systems, and production observability stacks supporting globally distributed workloads. Strong background in incident response, Linux systems, networking, distributed systems troubleshooting, and infrastructure automation using Terraform and Python. Focused on end-to-end ownership of production infrastructure platforms while partnering closely with engineering teams to improve reliability, security, deployment consistency, and operational efficiency.

Thomas S.
Available Now
Verified in SoftDoesThomas S.
Technical leader at a company AI Core
DE 🇩🇪English (C1)Team Lead
LeadershipMentorshipGenerative AIPyTorch

Technical leader at a company AI Core, building and operating cloud-native AI platform services from prototype to production. Hands-on across backend engineering, distributed systems, and Kubernetes, driving technical direction and delivery across teams without direct authority.

Thomas S.
Available Now
Thomas S.Verified in SoftDoes
Technical leader at a company AI Core
DE 🇩🇪English (C1)Team Lead
LeadershipMentorshipGenerative AIPyTorch

Technical leader at a company AI Core, building and operating cloud-native AI platform services from prototype to production. Hands-on across backend engineering, distributed systems, and Kubernetes, driving technical direction and delivery across teams without direct authority.

Thomas S.
Available Now
Thomas S.Verified in SoftDoes
Technical leader at a company AI Core
DE 🇩🇪English (C1)Team Lead
LeadershipMentorshipGenerative AIPyTorch

Technical leader at a company AI Core, building and operating cloud-native AI platform services from prototype to production. Hands-on across backend engineering, distributed systems, and Kubernetes, driving technical direction and delivery across teams without direct authority.

Tzechung K.
Available Now
Verified in SoftDoesTzechung K.
Lead AI/ML Developer
US 🇺🇸English (Native)Senior
Claude CodeOpenAI CodexGoogle AntigravityLLM Fine-Tuning

Software Engineer with 10+ years of AI/ML experience and proven expertise in developing LLM-powered applications, generative AI systems, and ad engines. Strong track record of scaling ML platforms from inception to 1,500+ users at a company, building production AI systems, and creating innovative ad solutions. Experienced in full software development lifecycle with deep knowledge of PyTorch, TensorFlow, and modern AI frameworks including LangChain, LangGraph, and various LLM APIs.

Tzechung K.
Available Now
Tzechung K.Verified in SoftDoes
Lead AI/ML Developer
US 🇺🇸English (Native)Senior
Claude CodeOpenAI CodexGoogle AntigravityLLM Fine-Tuning

Software Engineer with 10+ years of AI/ML experience and proven expertise in developing LLM-powered applications, generative AI systems, and ad engines. Strong track record of scaling ML platforms from inception to 1,500+ users at a company, building production AI systems, and creating innovative ad solutions. Experienced in full software development lifecycle with deep knowledge of PyTorch, TensorFlow, and modern AI frameworks including LangChain, LangGraph, and various LLM APIs.

Tzechung K.
Available Now
Tzechung K.Verified in SoftDoes
Lead AI/ML Developer
US 🇺🇸English (Native)Senior
Claude CodeOpenAI CodexGoogle AntigravityLLM Fine-Tuning

Software Engineer with 10+ years of AI/ML experience and proven expertise in developing LLM-powered applications, generative AI systems, and ad engines. Strong track record of scaling ML platforms from inception to 1,500+ users at a company, building production AI systems, and creating innovative ad solutions. Experienced in full software development lifecycle with deep knowledge of PyTorch, TensorFlow, and modern AI frameworks including LangChain, LangGraph, and various LLM APIs.

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What our Amazon Virtual Assistants can build

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How to hire a Amazon Virtual Assistant

01
BROWSE PROFILESRIGHT NOW

Fill out a short form and see who's on the bench. Real profiles, verified histories.

02
Interview1-3 DAYS

Tell us what you need. We propose two or three candidates from the bench; you interview them directly.

03
OnboardWEEK ONE

Your engineer starts on your project. Contract, payments, and the guarantee run through us.

US VS. THE DATABASE

Time to Start
Talent Quality
Technical Vetting
Flexibility
Operational Overhead
Cost Efficiency
cursor
<SoftDoes>
Time to Start
1-2 weeks
Talent Quality
Senior-only engineers
Technical Vetting
Multi-stage screening
Flexibility
Scale up or down anytime
Operational Overhead
As managed as you want
Cost Efficiency
Competitive, fee-free
Talent Marketplaces
Time to Start
1-3 months
Talent Quality
Mixed experience levels
Technical Vetting
One screen, then gone
Flexibility
Contract restrictions
Operational Overhead
Partially managed
Cost Efficiency
Agency markup
In-House Hiring
Time to Start
2-6 months
Talent Quality
Depends on market
Technical Vetting
Internal responsibility
Flexibility
Long-term commitment
Operational Overhead
Fully internal
Cost Efficiency
Highest total cost

Frequently Asked Questions

Everything you need to know about deploying, scaling, and securing your neural agents with SoftDoes. Can’t find an answer?

How long does it take to hire an Amazon Virtual Assistant through SoftDoes?

Most placements happen within seven to fourteen business days, depending on role complexity and specific requirements. For straightforward tasks like customer service and order processing, expect the VA to reach productive output within two to four weeks of onboarding. More complex responsibilities such as PPC management, strategic listing optimization, and Amazon product research typically require six to eight weeks before the VA operates with full autonomy. SoftDoes accelerates this timeline by matching you with specialists who already have hands on experience with Amazon Seller Central and the essential tools your business relies on.

What does it cost to hire an Amazon Virtual Assistant?

Costs depend on geography, experience level, and scope. Hourly rates for Amazon VAs range from $3 to $40. VAs from Southeast Asia typically charge $3 to $8 per hour for basic tasks like data entry, customer replies, and order management. Experienced Amazon VAs who handle PPC management, inventory management, and Amazon listing optimization can charge $15 to $40 per hour. Amazon PPC Experts charge $15 to $225 per hour depending on specialization and campaign complexity. Amazon Product Sourcing Experts earn $50 to $150 per hour. Working through a managed talent delivery partner like SoftDoes typically costs more than a raw freelance hire but includes vetting, onboarding support, replacement guarantees, and ongoing quality assurance, which reduces total cost of ownership significantly compared to cycling through multiple bad hires.

What engagement models are available (dedicated hire, pod, contract)?

Three primary models exist. A freelance or contract engagement works well for time consuming but clearly scoped tasks where you need a few hours per day or project based support. A dedicated remote hire (part time or full time) provides consistency and deeper integration with your team for ongoing day to day operations. A managed pod combines multiple specialists (for example, a PPC specialist, a listing optimization expert, and a customer service VA) under coordinated management, ideal for companies that want comprehensive Amazon virtual assistant services without building and managing the team themselves. SoftDoes supports all three models and can help you transition between them as your business grows.

How do you ensure time zone alignment with an Amazon Virtual Assistant?

Time zone alignment starts with being explicit in your job post and sourcing strategy. VAs from the Philippines and Southeast Asia offer strong overlap with US Pacific hours. Eastern European VAs align well with US Eastern time zones. SoftDoes, as a North America focused partner through our [talent network](/talent), prioritizes overlap windows that ensure real time collaboration during your core business hours while also enabling asynchronous work for tasks that do not require immediate interaction. The key is defining which tasks need synchronous communication (escalations, meetings) and which can run independently, then structuring coverage accordingly.

How does SoftDoes technically vet an Amazon Virtual Assistant?

SoftDoes' vetting process goes well beyond reviewing resumes. Every candidate undergoes a technical assessment that includes real Seller Central tasks (resolving suppressed listings, managing inventory, navigating account health dashboards), a PPC case study (designing a campaign structure, forecasting ROI, analyzing keyword performance), and a listing optimization exercise. English fluency is tested through live conversation, not just written samples. Reference checks focus specifically on prior Amazon clients and measurable outcomes. Candidates must demonstrate familiarity with current Amazon policies, compliance requirements, and tools like Helium 10 and Jungle Scout. Only candidates who pass every stage are presented to clients, and a guarantee period ensures you have recourse if the match does not meet expectations.

What happens if the Amazon Virtual Assistant isn't the right fit, or I need to scale up or down?

SoftDoes provides replacement guarantees as a core part of every engagement. If an Amazon VA is not performing or is not the right cultural fit, a replacement is sourced and transitioned without restarting the full hiring process or disrupting your operations. Scaling up is handled the same way: when your catalog expands, seasonal demand spikes, or you need specialists for additional Amazon related tasks, SoftDoes adds vetted talent to your team quickly. Scaling down is equally straightforward, with flexible contracts that do not lock you into rigid headcount. VAs provide flexibility to scale involvement based on needs, which is exactly the model SoftDoes is built around.

How to Hire an Amazon Virtual Assistant

Most Amazon sellers burn hours every week triaging support tickets, manually adjusting PPC bids, and firefighting suppressed listings instead of focusing on growth. The cost is not just time but missed revenue, declining margins, and operational fragility. This guide walks you through what an Amazon Virtual Assistant actually does, how to define the role internally, where to find and vet candidates, how to onboard effectively, and how to recognize a great hire from a risky one so you can build a reliable operations backbone for your Amazon business.

What an Amazon Virtual Assistant Actually Does and Why It Matters

The Expanding Scope of a Modern Amazon VA

An Amazon Virtual Assistant is not an admin who answers emails. In practice, this role functions as an e-commerce operations specialist who manages the daily mechanics of your Amazon seller central account so leadership can focus on product strategy and expansion. The scope has grown significantly as the Amazon marketplace has become more competitive and policy driven.

Here is what skilled Amazon VAs handle on a daily and weekly basis:

  • Amazon Seller Central management: Uploading and maintaining product listings, resolving suppressed or inactive ASINs, performing keyword research, and managing listing optimization across your entire catalog.
  • PPC management and advertising: Setting up and adjusting Amazon advertising campaigns, monitoring spend, analyzing ACoS, and refining bids and targeting for profitable growth. Virtual assistants often have specialized skill sets in areas like SEO and PPC management.
  • Customer service and feedback: Responding to buyer messages, processing refunds and returns, handling negative feedback and A to Z claims under Amazon policy, and protecting seller metrics.
  • Inventory management and order monitoring: Tracking shipments, coordinating with suppliers, managing Amazon FBA inbound shipments, monitoring inventory levels, and setting reorder alerts to prevent stockouts.
  • Product research and sourcing: Conducting Amazon product research using essential tools like Helium 10 and Jungle Scout, evaluating market trends, and supporting product sourcing decisions.
  • Reporting and compliance: Tracking KPIs (sales growth, return rates, profit margins, advertising ROI), preparing performance dashboards, and staying current with Amazon's evolving listing rules, brand registry requirements, and seller policies.

Amazon VAs need strong written English, hands on experience with Seller Central, familiarity with Amazon advertising tools, an analytical mindset, and the discipline to follow standard operating procedures while proactively surfacing issues before they escalate.

Why Getting This Hire Right Is a Strategic Priority

Hiring the right Amazon Virtual Assistant is not a staffing decision. It is an operational one. Here are the concrete business outcomes at stake:

  • Reclaim valuable time for strategy: Offloading day to day maintenance enables a focus on product development and business expansion. Hiring a VA saves hours each day for business owners, shifting leadership attention from routine tasks to scaling the Amazon store.
  • Reduce operational costs: Experienced Amazon VAs can deliver reliable execution at a fraction of the cost of a full time, in house hire once you factor in overhead, benefits, and onboarding. Hiring a VA is often more affordable than full time employees while providing the same coverage of essential tasks.
  • Scale without fragility: A vetted VA or VA team can absorb seasonal demand spikes, maintain smooth operations across time zones, and reduce the error rates that lead to policy violations or account suspensions. Routine processes can be executed consistently by virtual assistants without personal bandwidth reliance.
  • Drive measurable performance gains: Proper listing creation, disciplined PPC management, tighter inventory management, and proactive feedback handling directly increase sales, improve conversion rates, and protect profit margins. VAs can improve operational efficiency in Amazon businesses by ensuring nothing falls through the cracks.

How to Prepare Internally Before Opening the Role

Defining Your Needs Before You Hire

Before you write a job post or contact a single candidate, invest time in defining exactly what this role needs to accomplish. Skipping this step is the single most common reason hiring goes sideways.

Project Scope and Requirements

Map every operational area you expect the Amazon VA to handle. Start with a list: listing optimization, PPC management, customer service, order processing, inventory tracking, data entry, product research, feedback management. Then document your current pain points (delayed reviews? late shipments? rising ACoS?), your volume (number of SKUs, monthly orders, ad spend), and the frequency of each task. Clearly defining the role and responsibilities of a virtual assistant is crucial in the hiring process. Determine which metrics matter most and what essential tools are already in place.

Team Structure and Engagement Model

Decide where this hire fits. Will the Amazon VA report directly to you, or through an operations manager? How will communication and escalation work? Define whether you need a few hours per day or full time coverage, and whether overlap with your internal team's working hours is essential for seamless collaboration.

In House vs. Dedicated Remote Talent

Compare your options honestly. Hiring in house gives you higher control and live collaboration but at significantly higher cost. Offshore or remote virtual assistants offer cost savings and flexible hours. A managed service or talent delivery partner adds overhead but removes the recruitment burden and provides replacement guarantees. Your choice depends on budget, control needs, speed, and whether this is a long term commitment or a project based engagement. Access to global talent allows for high quality support at competitive rates.

Writing a Job Description That Attracts the Right Amazon VA Candidates

A generic job post attracts generic candidates. A strong job description for Amazon virtual assistant jobs covers four elements:

  • Mission: Define the outcome you are hiring for, not just the tasks. For example: "Keep our listings healthy, advertising efficient, customers satisfied, and daily operations scalable." Set measurable goals wherever possible.
  • Stack and context: Specify the platforms and tools (Amazon Seller Central or Vendor Central, PPC tools, Jungle Scout, Helium 10, reporting dashboards), your catalog size, typical ad spend, and which marketplace regions you operate in.
  • Team structure: Clarify who the VA reports to, who they will interact with (team members, suppliers), their decision making latitude, and escalation paths.
  • Growth and impact: Describe how this role can evolve as your business grows. Can the VA take on more ownership over various aspects of operations? Is there a path to managing others or leading a pod? What does success look like in terms of metrics and growth opportunities?

Also include required experience (years, Amazon specific work), preferred but optional skills, hours and time zone expectations, and compensation range with regional transparency.

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How to Source, Evaluate, and Onboard Your Amazon Virtual Assistant

The Hiring and Vetting Process

Sourcing Strategy

You can find Amazon VAs through several channels, each with trade offs:

  • Freelance platforms (Upwork, Fiverr, FreeUp): Flexible and cost accessible, but quality varies widely. Utilizing reputable platforms can help source skilled virtual assistants.
  • Specialized talent networks (OnlineJobs.ph, VA Fast Lane, EcomVA): These focus on Amazon related tasks and often present pre screened VA candidates who have already passed assessment tests.
  • Talent delivery partners (like SoftDoes): More rigorous vetting, team delivery models, replacement guarantees, and faster ramp. Higher investment upfront, but dramatically lower risk of a bad hire.
  • Referrals from other Amazon sellers: Often the highest signal source, though limited in volume.

The right sourcing strategy depends on your urgency, budget, and risk tolerance. If you need reliable execution without spending weeks in the hiring process, a delivery partner with pre vetted specialists is typically the fastest path.

Vetting Beyond the Resume

Resumes and portfolios only tell part of the story. To identify experienced Amazon VAs who can hit the ground running, your vetting process should include:

  • Domain specific evidence: Ask candidates for real examples. What PPC campaigns have they managed? What ACoS did they achieve? How many SKUs have they handled? How did they resolve a suppressed listing?
  • Practical task: Testing practical skills through trial assignments can help evaluate virtual assistant candidates. Give them a real listing to optimize or ask them to design a PPC budget and forecast ROI. This reveals capability far better than interview answers.
  • Analytical and problem solving interview: Pose a scenario: "Your bestseller listing gets suppressed. Walk me through diagnosing and fixing it." Look for structured thinking and familiarity with Amazon's policy landscape.
  • Culture fit and reliability: Assess communication habits, accountability, time management, and willingness to learn new tools. Ask how they handle shifting priorities and set deadlines.

Check references, especially from prior Amazon clients. Experienced virtual assistants can quickly resolve issues in Seller Central and handle customer complaints, and references will confirm whether that is actually true.

Building Momentum: The 30/60/90 Day Onboarding Framework

Structured training and onboarding should be conducted for virtual assistants. Expect full productivity on straightforward tasks (customer service, order management) within two to four weeks. More complex work like PPC management and strategic listing optimization often takes six to eight weeks.

First 30 days: Assign a small, real project. Provide documented standard operating procedures (and video tutorials for complex workflows), define a communication cadence with weekly check ins, and establish clear KPIs. Limit access privileges until trust is established. Amazon does not support sharing a single sign in across multiple users, and Amazon recommends using user permissions with limited access for virtual assistants. Implementing security measures like multi factor authentication is essential for account protection, and regular reviews of user permissions help maintain account security.

Days 31 to 60: Transition the VA into a regular workload. Begin delegating routine tasks with more autonomy. Monitor performance closely using project management and tracking tools. Start handing off steady, repeatable work and assign tasks with increasing complexity.

Days 61 to 90: Evaluate performance against goals. Adjust scope based on results. Provide direct feedback, discuss expanded responsibilities, and consider whether it is time to scale with additional VAs or assign more strategic Amazon related tasks.

Retention: The best virtual assistants stay when they feel ownership. Offer consistent feedback, fair compensation increases tied to performance, access to new tools and training, and a clear growth path. Reducing personal workload helps maintain work life balance, and the same applies to your VA. Regular communication methods enhance collaboration with virtual assistants and help them maintain focus on what matters most.

Choosing Wisely: Red Flags, Green Flags, and the Right Partner

Warning Signs and Winning Signals in the Hiring Process

Red flags:

  • Vague or generic experience claims ("I have experience with Amazon") without specifics about ASINs managed, PPC results, or seller central account work.
  • Poor communication during hiring: slow responses, unclear answers, grammar issues, or inability to pass a basic test task.
  • No familiarity with Amazon's current policies, frequent changes, or compliance requirements.
  • Rates that seem too good to be true with no transparency about experience level. An entry level VA at rock bottom pricing often leads to costly corrections and wasted time.

Green flags:

  • Provides concrete data: "I managed PPC campaigns that achieved an ACoS of X%" or "I handled listing optimization across Y SKUs and increased sales by Z%."
  • Solid references from Amazon sellers or brands, with resumes that quantify scope and name specific tools used.
  • Proactive problem solving during the interview: notices gaps, asks diagnostic questions, and demonstrates analytical thinking.
  • Reliable work ethic, clear English, willingness to follow SOPs, and enthusiasm for learning. These candidates hit the ground running.

Why SoftDoes Is Built for This Hire

Most companies that try to hire Amazon virtual assistants on their own cycle through multiple candidates before finding someone reliable. SoftDoes eliminates that trial and error. As a North America focused talent delivery partner, SoftDoes provides access to carefully vetted senior talent through a team delivery model, not isolated freelancers. That means built in redundancy, knowledge transfer, and no single points of failure.

SoftDoes offers replacement and scaling guarantees, so if an Amazon VA is not the right fit or your business grows and you need to expand, adjustments happen quickly without restarting the hiring process. Engagement models flex from a single dedicated specialist to a full operations pod, giving you the ability to scale your Amazon virtual assistant services as industry demand and your catalog evolve.

Through our services, clients avoid the most common and expensive pitfalls: slow hiring cycles, mismatched expertise, and the operational risk of depending on a single freelancer with no backup. For companies in regulated industries, SoftDoes' compliance and quality assurance culture adds another layer of security.

Ready to Hire an Amazon Virtual Assistant?

Stop spending weeks sifting through VA candidates and hoping for the best. Schedule a discovery call with SoftDoes and we will help you define the exact role, map your Amazon related tasks, determine the right engagement model, and present pre vetted specialists within days. Whether you need help with PPC management, listing creation, inventory management, or full Amazon store operations, the next step is a conversation.

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