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About This Role
DESCRIPTION
The Forward Deployed Engineering (FDE) team within Business Innovation & AI (BIA) is looking for a Senior Technical Program Manager who builds. FDE embeds directly with teams across Amazon Global Operations Services (GOS), co-builds with them from day one, and ships working AI-powered solutions. Engagements range from short builds to multi-quarter programs. The impact here is internal: you build for the GOS org, not for external customers. We increasingly look for opportunities to replace manual processes and web-based interfaces with AI agents, so a lot of what you build will be agentic.
This is an FDE role first, with a data engineering edge. The core of the job is building AI and agentic solutions embedded with operational teams. On top of that, you own the team’s data and data-engineering strategy and infrastructure, the pipelines and architecture and data-quality decisions our AI work depends on.
This is a builder role. We’re looking for people who are already shipping AI solutions with the current generation of tooling, not people who are newly exploring AI or have only done some prompt engineering. The engineers who do well here are hands-on with agentic harnesses like Claude Code, Kiro, and Codex, and are plugged into the AI builder community closely enough to know what changed this month.
Key job responsibilities
Build AI & Agentic Solutions, Embedded (the primary work)
- Embed with GOS operational teams, co-build from day one, and ship AI-powered solutions that change how work gets done
- Look for opportunities to replace manual processes and web-based interfaces with AI agents, and build those agents end to end
- Build with the current AI tooling yourself, including agentic harnesses (Claude Code, Kiro, Codex), LLM application frameworks, and RAG, and take prototypes to production
Program Delivery
- Drive the planning, coordination, and delivery of FDE’s AI programs, managing risk and dependencies without relying on direct authority
- Turn ambiguous operational problems into shipped solutions, and own the longer-horizon programs where the answer takes more than one build cycle
Data & Data-Engineering Strategy (the DE edge)
- Own the team’s data and data-engineering strategy and infrastructure: the architecture, technology choices, and roadmap our AI work depends on
- Make the calls on how operational data becomes AI-ready, and keep data quality, lineage, and freshness where the team can trust it
- Connect source data to what the customer actually needs: find the right source, get the subscription in place, and build the pipeline that keeps it flowing
- Make and defend the opinionated architecture calls (lakehouse vs. warehouse, batch vs. streaming, how far to normalize a domain), grounded in each use case
Raising the Bar
- Define the reusable patterns and standards that raise the bar for the whole team, and mentor engineers on them
A day in the life
You start the morning embedded with an operations team, working through a manual workflow you’re replacing with an agent. By mid-morning you’re building against it with current tooling and putting the next iteration in front of them. In the afternoon you switch to the data side, making the call on how a new data domain gets modeled and subscribed into the platform so the agent has what it needs. You close the day reviewing a teammate’s design against the patterns you set, and re-cutting the roadmap for a program that has two quarters left to run.
About the team
Business Innovation & AI (BIA) finds the hardest, highest-value problems across Amazon Global Operations Services and builds AI-powered solutions that measurably change how work gets done. Forward Deployed Engineering (FDE) is BIA’s technical execution arm: a small, senior team with high autonomy and a bias for shipping. We embed with operational teams in the GOS org, co-build from day one, ship working solutions, and hand scalable patterns off to the broader organization. We don’t just automate the existing process; we redesign the work around what AI and agents make possible.BASIC QUALIFICATIONS
- 5+ years of technical program management experience
- Experience developing, deploying and managing AI products at scale
- Knowledge of data engineering pipelines, cloud solutions, ETL management, databases, visualizations and analytical platforms
- Experience with SQL and Python scripting
- Bachelor's degree in computer science, engineering, mathematics or equivalent, or Bachelor's degree or above in computer science, engineering, analytics, mathematics, statistics, IT or equivalent
- Experience with AWS data services (S3, Glue, Athena, Redshift, or an equivalent cloud data stack)
- Experience driving technical programs across multiple teams without direct authority
PREFERRED QUALIFICATIONS
- Experience defining and driving an analytics roadmap
- 5+ years of data modeling experience
- A track record of shipping bespoke AI and agentic solutions from scratch, and active engagement in AI builder communities
- Experience building AI agents that replace manual or UI-driven workflows
- Familiarity with Amazon’s internal data ecosystem (Andes, data subscriptions, and getting access to source data across orgs)
- Fluency with the current GenAI tooling landscape (Amazon Bedrock, agent frameworks, LLM-powered workflows)
- Experience with Infrastructure as Code (CDK, CloudFormation, Terraform)
- Experience with real-time or streaming data infrastructure (Kinesis, Kafka, Flink)
- Experience mentoring engineers and defining reusable patterns that scale beyond a single team
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, TN, Nashville - 141,300.00 - 191,100.00 USD annually
USA, WA, Bellevue - 148,700.00 - 201,200.00 USD annually
Salary Context
This $141K-$201K range is below the median 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.com, 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. This role's midpoint ($171K) sits 22% below the category median. Disclosed range: $141K to $201K.
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.com AI Hiring
Amazon.com has 72 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, Data Scientist, AI Product Manager. Positions span Seattle, WA, US, New York, NY, US, Bellevue, WA, US. Compensation range: $129K - $327K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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
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