Interested in this AI Product Manager role at Amazon Web Services?
Apply Now →Skills & Technologies
About This Role
DESCRIPTION
Amazon Bedrock AgentCore is building the foundational services developers use to create, deploy, and operate production-ready AI agents securely at scale. Our team owns the developer experience across AgentCore—from a developer's first interaction through building, testing, deploying, and operating agents in production.
This includes the AgentCore CLI, SDKs, infrastructure-as-code tooling, console experiences, configuration models, deployment workflows, and other interfaces through which developers use AgentCore. We bring the capabilities of AgentCore Runtime, Memory, Gateway, Identity, Observability, Browser, Code Interpreter, Evaluations, and other services together into a coherent experience. Our broader portfolio also includes AgentCore harness and new developer-facing service offerings planned for AWS re:Invent 2026 and beyond.
We are looking for a Senior Software Development Engineer to set the team's technical vision and lead its architecture. You will determine how developers interact with AgentCore across local development, configuration, deployment, testing, and production operations. You will turn ambiguous customer needs into durable technical designs and lead the delivery of capabilities that make sophisticated agent systems easier to build and operate.
You will work directly with principal engineers, product managers, and engineering leaders across AgentCore service teams. You will establish consistent abstractions and APIs, shape cross-service contracts, and make architectural decisions that affect the complete AgentCore development lifecycle.
The technical challenges span both developer tooling and distributed systems. You will design configuration and resource models, SDK and CLI abstractions, infrastructure constructs, console workflows, deployment automation, and diagnostic experiences. You will address problems involving cross-service orchestration, permissions and resource wiring, API evolution, state management, versioning, reliability, scalability, and the consistency of experiences across multiple interfaces.
A central part of this role is serving as a voice of the customer for some of AgentCore's most impactful features. You will work backward from how developers actually build agents, identify friction across service boundaries, and ensure that developer experience is considered early in product and architecture decisions—not added after individual services have been designed.
You will help determine which complexities AgentCore should manage automatically, where customers need explicit control, and how developers can move between guided workflows and advanced customization. This may include simplifying tool and memory integration, automating permissions and resource wiring, shortening the local build-test-deploy loop, improving operational diagnostics, introducing richer templates and skills, and creating safer ways to evaluate and release agent changes.
As a senior engineer, you will lead complex, cross-team initiatives from concept through production, make long-term architectural decisions, and raise the engineering bar through implementation, design reviews, and mentorship. You will be expected to influence beyond the team's immediate codebase and help define what a high-quality agent development experience should look like on AWS.
Key job responsibilities
- Set the technical direction and architecture for the AgentCore developer experience across APIs, SDKs, CLI, console, infrastructure as code, and local development workflows.
- Design cohesive abstractions, configuration models, and workflows that bring AgentCore services together into a consistent end-to-end experience.
- Lead the design and delivery of new developer-facing services and capabilities, from early concepts through production launch and operation.
- Simplify how developers connect models, tools, MCP servers, memory, identity, and custom environments using intuitive defaults, templates, and automation.
- Shorten the build-test-evaluate-deploy loop while preserving the control and extensibility advanced builders need.
- Design lifecycle capabilities such as versioning, deployment, rollback, evaluations, optimization, diagnostics, and operational visibility.
- Build highly available control-plane and data-plane components that support secure, scalable, and cost-efficient developer experiences.
- Work directly with customers to identify friction, represent their needs in technical and product decisions, and accelerate their path to production.
- Partner with principal engineers, product managers, and AgentCore service teams to shape cross-service APIs, architecture, and product strategy.
- Raise the engineering bar through implementation, design and code reviews, operational readiness, and mentoring.
BASIC QUALIFICATIONS
- 5+ years of non-internship professional software development experience
- 5+ years of programming with at least one software programming language experience
- 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience as a mentor, tech lead or leading an engineering team
PREFERRED QUALIFICATIONS
- 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent
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 - 184,900.00 - 250,200.00 USD annually
Salary Context
This $184K-$250K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $193K across 99 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 9,653 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Amazon Web Services, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $220,200 based on 862 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $184K to $250K.
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 Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
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).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.