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About This Role
DESCRIPTION
This position sits within the AWS Specialist and Partner Organization (ASP). Specialists own the end-to-end go-to-market strategy for their technology domains and provide the business and technical expertise that helps our customers succeed. Partner teams own the strategy, recruiting, development, and growth of our key technology and consulting partners. Together, they give our customers the expertise and scale they need to build innovative solutions for their most complex challenges.
Customer experience is being reinvented by applied AI, and Amazon Connect sits at the center of that shift. Our team turns the newest Amazon Connect AI capabilities into working systems and hands-on experiences that customers, sales teams, and partners can actually use. What we build shortens the path from “is this possible?” to “here’s how it works,” accelerates adoption of applied AI on Amazon Connect, and feeds real-world signal back into the product roadmap. The reference architectures, demos, and internal assets you create are often the first thing a customer sees, and the fastest way a global sales team can show the art of the possible.
The Applied AI Solutions Architecture team is hiring a hands-on Solutions Architect to design and deliver AI-first solutions on Amazon Connect. The role has two halves: you build working systems, reference architectures, and internal assets that advance org-level initiatives, and you build Live demos that let sales teams, customers, and partners use production-quality Amazon Connect AI agents.
You work across the full stack, from the user-facing experience down to the integrations and data behind it, anchored on Amazon Connect. The work covers five areas:
Prompt configuration: Design, test, and tune prompts and system instructions for Amazon Connect AI agents, including self-service agents, answer recommendation agents, and custom orchestrator agents. Evaluate LLM behavior and tune for reliable performance.
Agent memory and context: Build short-term, long-term, and session memory, manage context windows, and structure state so agents stay coherent and personalized across multi-turn and multi-agent interactions.
Tool configuration: Build the integrations agents use to take action (APIs, Lambda functions, data connectors, knowledge bases), configure MCP (Model Context Protocol) servers for standardized tool discovery and invocation, and enable A2A (Agent-to-Agent) patterns for multi-agent orchestration.
Data readiness: Assemble and structure realistic datasets and knowledge bases (sample CRMs, ticketing systems, order management) so agents retrieve and act on the right information.
Well-Architected: Apply the AWS Well-Architected Framework across operational excellence, security, reliability, performance efficiency, cost optimization, and sustainability.
Key job responsibilities
- Org initiatives: Deliver AI-first solutions for org-level initiatives, including working systems, reusable reference patterns, and internal assets on Amazon Connect that help the broader organization adopt applied AI.
- Live demos: Design, build, and maintain Live demo environments showcasing Amazon Connect AI agents. Turn emerging use cases and new product capabilities into hands-on experiences.
- Full-stack development: Build front-end experiences and back-end services, write REST APIs and web services, and integrate agentic and generative AI into working applications.
- Agent development: Configure Amazon Connect AI agents end to end, covering agent creation, prompt engineering, guardrails, LLM evaluation and tuning, and memory and context management across turns and sessions.
- Tools and multi-agent orchestration: Deploy MCP servers that expose tools, data sources, and APIs in a standardized format for dynamic discovery and invocation. Architect A2A patterns so Connect agents collaborate with specialized agents for billing, order management, and IT support.
- Integrations and data: Build serverless integrations with AWS Lambda, API Gateway, and Step Functions in Python and Node.js. Architect secure access to Amazon DynamoDB, RDS, S3, OpenSearch, Kendra, and Knowledge Bases for Amazon Bedrock for agent tool use and RAG.
- Security and Well-Architected: Apply least-privilege access, data protection, and guardrails, account for HIPAA and GDPR where relevant, and hold every solution to the Well-Architected pillars.
- Validation: Test and evaluate agent performance against defined success criteria so each solution and demo is reliable and repeatable.
- Knowledge sharing: Create reusable artifacts (reference architectures, playbooks, sample code, prompt libraries, data readiness checklists) for the Amazon Connect SA community and partner ecosystem.
- Service team collaboration: Share what you learn with the Amazon Connect and Amazon Bedrock product teams and contribute to roadmap prioritization.
A day in the life
- Build and test Amazon Connect AI agent configurations for internal solutions and Live demos.
- Write front-end code, REST APIs, and back-end services.
- Tune prompts and agent memory, then evaluate the results against your success criteria.
- Configure MCP servers and A2A handoffs, and wire up knowledge bases for RAG.
- Review a teammate’s demo and give prescriptive guidance.
- Document patterns, and join the weekly Amazon Connect service team sync.
About the team
The Applied AI Solutions Architecture team builds the AI-first solutions and Live demos that show what Amazon Connect can do. We work at the leading edge of the product, translating brand-new AI agent capabilities into reference architectures, sample code, and interactive experiences that the broader SA community, global sales teams, and partners rely on every day. Our work matters because it directly accelerates how quickly customers can adopt applied AI to transform their customer experience, and because the patterns we prove out and the feedback we surface help shape where Amazon Connect goes next.
We’re a globally diverse team with a broad mix of experience levels and backgrounds, and we invest in knowledge sharing and mentorship. We care about your growth and assign projects to help each person develop into a better-rounded professional. We also value work-life harmony and a flexible schedule, because doing your best work should never come at the expense of your life outside it.
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
- 7+ years of specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics) experience
- 3+ years of design, implementation, or consulting in applications and infrastructures experience
- 3+ years of hands-on coding experience with REST APIs and web services, including integrating agentic or generative AI into working applications.
- Full-stack development experience across front-end and back-end.
- Cloud experience (AWS preferred) across compute, database, integration, identity, networking, and automated packaging and deployment.
- Hands-on experience with large language models (LLMs), prompt engineering, agent memory and context management, retrieval-augmented generation (RAG), and model evaluation.
- Familiarity with MCP (Model Context Protocol) and A2A (Agent-to-Agent) interoperability protocols.
PREFERRED QUALIFICATIONS
- Knowledge of security and compliance standards including HIPAA and GDPR
- Hands-on experience with Amazon Connect and its AI capabilities.
- Hands-on experience with Amazon Bedrock, including model invocation, agent creation, knowledge base configuration, and guardrails.
- 2+ years designing and implementing conversational AI (voice or chat) with a framework such as Amazon Lex.
- Experience with agentic AI patterns such as multi-agent orchestration, tool use, function calling, chain-of-thought reasoning, and autonomous agent workflows.
- Experience building and deploying MCP servers that expose enterprise tools and APIs for dynamic agent tool discovery.
- Experience applying the AWS Well-Architected Framework to production systems.
- Experience with automation and scripting (for example, Terraform and Python).
- Experience building demos, proofs-of-concept, or interactive product experiences.
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, TX, Austin - 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: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($180K) sits 15% below the category median. Disclosed range: $153K to $207K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Amazon Web Services AI Hiring
Amazon Web Services has 79 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Data Scientist, Research Engineer. Positions span New York, NY, US, Bellevue, WA, US, Atlanta, GA, US. Compensation range: $97K - $299K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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