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About This Role
DESCRIPTION
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This position is part of the AWS Specialist and Partner Organization (ASP). Specialists own the end\-to\-end go\-to\-market strategy for their respective technology domains, providing the business and technical expertise to help our customers succeed. Partner teams own the strategy, recruiting, development, and growth of our key technology and consulting partners. Together they provide our customers with the expertise and scale needed to build innovative solutions for their most complex challenges.
The Applied AI Solutions Architecture team within AWS is seeking a hands\-on, customer\-obsessed Solutions Architect to accelerate customer adoption of Amazon Connect's AI capabilities.
As an Applied AI Solutions Architect, you will be embedded with customers to help them prepare their Amazon Connect implementations for production by focusing on three critical pillars of agentic AI:
Model Selection — Guiding customers through evaluating and selecting the right foundation models (via Amazon Bedrock) for their contact center use cases, balancing latency, accuracy, cost, and compliance requirements.
Prompt Configuration — Designing, testing, and optimizing AI prompts and system instructions for Amazon Connect AI agents, including self\-service agents, answer recommendation agents, and custom orchestrator agents.
Tool Configuration — Architecting and building the tool integrations (APIs, Lambda functions, data connectors, knowledge bases) that agentic AI systems use to take actions on behalf of customers and agents — including configuring MCP (Model Context Protocol) servers for standardized tool discovery and invocation, and enabling A2A (Agent\-to\-Agent) communication patterns for multi\-agent orchestration across enterprise systems.
A critical dimension of this role is Customer Data Readiness — assessing, preparing, and structuring customer data assets so that AI agents can reliably access, retrieve, and act on the right information. You will help customers evaluate their data landscape, identify gaps, establish data pipelines, and ensure their knowledge bases, CRMs, and backend systems are AI\-ready before agents go live.
You will work at the intersection of contact center operations and applied AI, helping customers move from proof\-of\-concept to pre\-production for their Amazon Connect \+ Unlimited AI deployments. This is a deeply technical, hands\-on role — you will write code, build integrations, configure agents, and pair\-program with customer engineering teams.
Willingness to travel up to 25\-40% for on\-site customer engagements
Key job responsibilities
- Customer Engagement: Lead technical discovery sessions with customer teams to understand business requirements, existing contact center architecture, and AI readiness. Translate findings into actionable implementation plans.
- Customer Data Readiness: Conduct data readiness assessments to evaluate the quality, accessibility, structure, and governance of customer data assets (CRMs, knowledge bases, ticketing systems, order management, etc.). Identify data gaps, recommend remediation strategies, and help customers build the data foundation required for effective AI agent tool use and RAG\-powered responses.
- Agentic AI Implementation: Design and configure agentic AI solutions within Amazon Connect, including AI agent creation, AI prompt engineering, model selection, guardrail configuration, and tool/action integration.
- MCP Server Configuration: Design and deploy Model Context Protocol (MCP) servers that expose customer tools, data sources, and APIs in a standardized format — enabling AI agents to dynamically discover and invoke capabilities across the customer's technology stack.
- A2A (Agent\-to\-Agent) Integration: Architect Agent\-to\-Agent communication patterns that allow Amazon Connect AI agents to collaborate with specialized agents across the enterprise (e.g., billing agents, order management agents, IT support agents), enabling multi\-agent workflows that span organizational boundaries.
- Integration Development: Build serverless integrations using AWS Lambda, API Gateway, Step Functions, and scripting (Python, Node.js) to connect Amazon Connect AI agents with customer data systems (CRMs, ERPs, databases, knowledge bases).
- Cloud Data Access: Architect secure access patterns to cloud\-based data systems (Amazon DynamoDB, Amazon RDS, Amazon S3, Amazon OpenSearch, Amazon Kendra/Knowledge Bases for Bedrock) to power AI agent tool use and retrieval\-augmented generation (RAG).
- Pre\-Production Validation: Guide customers through testing, evaluation, and validation of AI agent performance against defined success criteria before production deployment.
- Knowledge Sharing: Create reusable artifacts (reference architectures, implementation guides, sample code, prompt libraries, data readiness checklists) that scale best practices across the Connect SA community and partner ecosystem.
- Service Team Collaboration: Provide feedback to Amazon Connect and Amazon Bedrock product teams based on real\-world customer implementations, contributing to product roadmap prioritization.
Key job responsibilities
- Customer Engagement: Lead technical discovery sessions with customer teams to understand business requirements, existing contact center architecture, and AI readiness. Translate findings into actionable implementation plans.
- Customer Data Readiness: Conduct data readiness assessments to evaluate the quality, accessibility, structure, and governance of customer data assets (CRMs, knowledge bases, ticketing systems, order management, etc.). Identify data gaps, recommend remediation strategies, and help customers build the data foundation required for effective AI agent tool use and RAG\-powered responses.
- Agentic AI Implementation: Design and configure agentic AI solutions within Amazon Connect, including AI agent creation, AI prompt engineering, model selection, guardrail configuration, and tool/action integration.
- MCP Server Configuration: Design and deploy Model Context Protocol (MCP) servers that expose customer tools, data sources, and APIs in a standardized format — enabling AI agents to dynamically discover and invoke capabilities across the customer's technology stack.
- A2A (Agent\-to\-Agent) Integration: Architect Agent\-to\-Agent communication patterns that allow Amazon Connect AI agents to collaborate with specialized agents across the enterprise (e.g., billing agents, order management agents, IT support agents), enabling multi\-agent workflows that span organizational boundaries.
- Integration Development: Build serverless integrations using AWS Lambda, API Gateway, Step Functions, and scripting (Python, Node.js) to connect Amazon Connect AI agents with customer data systems (CRMs, ERPs, databases, knowledge bases).
- Cloud Data Access: Architect secure access patterns to cloud\-based data systems (Amazon DynamoDB, Amazon RDS, Amazon S3, Amazon OpenSearch, Amazon Kendra/Knowledge Bases for Bedrock) to power AI agent tool use and retrieval\-augmented generation (RAG).
- Pre\-Production Validation: Guide customers through testing, evaluation, and validation of AI agent performance against defined success criteria before production deployment.
- Knowledge Sharing: Create reusable artifacts (reference architectures, implementation guides, sample code, prompt libraries, data readiness checklists) that scale best practices across the Connect SA community and partner ecosystem.
- Service Team Collaboration: Provide feedback to Amazon Connect and Amazon Bedrock product teams based on real\-world customer implementations, contributing to product roadmap prioritization.
A day in the life
A day in the life
Pair\-programming with customer developers to build and test AI agent configurations
- Designing prompt strategies and evaluating model performance across different foundation models
- Configuring MCP servers to expose customer APIs, databases, and tools in a standardized format for agent consumption
- Designing A2A workflows where Amazon Connect agents hand off to or collaborate with specialized agents across the customer's enterprise
- Configuring knowledge bases and data connectors for RAG\-powered agent responses
- Conducting architecture reviews and providing prescriptive guidance for production readiness
- Documenting implementation patterns and contributing to the team's knowledge base
Participating in weekly syncs with Connect service teams to share customer feedback and product insights
About the team
The Applied AI Solutions Architecture team is part of the AWS Specialist and Partner Organization (ASP). We are the technical bridge between Amazon Connect customers and the service teams building the next generation of AI\-powered contact center capabilities. Our team operates at the forefront of agentic AI adoption, helping customers become production\-ready with Amazon Connect's Unlimited AI features.
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
AWS values curiosity and connection. Our employee\-led and company\-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do.
Mentorship \& Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge\-sharing, mentorship and other career\-advancing resources here to help you develop into a better\-rounded professional.
Work/Life Balance
We value work\-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.BASIC QUALIFICATIONS
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- 6\+ years of design, implementation, or consulting in applications and infrastructures experience
- Experience with Amazon Connect or other enterprise contact center platforms (Genesys, Avaya, Cisco, NICE, Five9, etc.)
- Hands\-on experience with Amazon Bedrock, including model invocation, agent creation, knowledge base configuration, and guardrails
PREFERRED QUALIFICATIONS
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- 5\+ years of infrastructure architecture, database architecture and networking experience
- Experience with agentic AI patterns — multi\-agent orchestration, tool use, function calling, chain\-of\-thought reasoning, and autonomous agent workflows
- Hands\-on experience building and deploying MCP servers — exposing enterprise tools and APIs via Model Context Protocol for dynamic agent tool discovery and invocation
- Experience designing A2A (Agent\-to\-Agent) architectures — enabling specialized agents to collaborate across domains (e.g., billing, logistics, IT) through standardized agent communication protocols
- Proficiency with agentic IDEs such as Kiro, Cursor, or similar AI\-assisted development environments, including experience with agent hooks, agent steering, MCP server configuration, and spec\-driven development
- AWS certifications (Solutions Architect Professional, AI Practitioner, Machine Learning Specialty)
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how\-we\-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign\-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life \& AD\&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, NY, New York \- 169,000\.00 \- 228,600\.00 USD annually
USA, VA, Arlington \- 153,600\.00 \- 207,800\.00 USD annually
USA, WA, Seattle \- 153,600\.00 \- 207,800\.00 USD annually
Salary Context
This $153K-$207K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Amazon Web Services, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $153K to $207K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Amazon Web Services AI Hiring
Amazon Web Services has 78 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer, Research Scientist, AI Product Manager. Positions span Seattle, WA, US, San Francisco, CA, US, Arlington, VA, US. Compensation range: $177K - $295K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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