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About This Role
DESCRIPTION
AWS Specialist Technology Team (STT) is seeking a Software Development Manager to lead the Specialist GTM AI Tooling engineering team, the group building AI-native, agentic solutions that are fundamentally transforming how AWS Go-To-Market (GTM) teams turn customer opportunities into sales plays, and how those plays reach thousands of AWS field team members in the tools where they already work. If you want to build production agentic AI systems that impact thousands of users daily (not research prototypes, but real AI agents solving real problems), this is the role for you.
You will own and grow a team of Software Development Engineers responsible for building brand new and enhancing existing products that collectively connect AWS's go-to-market strategy to field execution:
- Specialist Knowledge Vault (SKV) - An AI-first knowledge service that uses purpose-built crawlers to index specialist content from a wide range of sources, which removes manual consolidation. SKV lets Technical Field Communities (TFCs) endorse golden content, add agent steering, build reusable playbooks, and flag outdated or incorrect content. SKV powers the AWS Specialist Agent, which more than 11,000 field team members used across nearly 100,000 conversations in its first three months.
- SKV Playbooks for GTM - Your team will own playbooks in SKV and extend them to natively represent GTM sales plays, including positioning guidance for every stage of the sales funnel, so AI agents deliver the right play to sellers with endorsed content and clear provenance. You will build the capabilities that let domain and industry leaders author with AI assistance, draft and test changes before they go live, detect duplicate or conflicting plays, and score playbook health, then migrate AWS's service-aligned GTM domains onto the platform.
- SKV Recommendations Engine - Your team will own the closed-loop AI/ML recommendation service that analyzes agent conversations, telemetry, and customer feedback signals to tell content owners what to fix next: knowledge gaps where the agent had no good answer, and inaccurate or outdated content it should no longer trust. Recommendations become next-best actions, such as updating a playbook, refreshing a first call deck, or creating new solution guidance, which agents can draft automatically for human review.
- Alchemy - The platform where AWS GTM teams capture and prioritize customer use cases and map first-party and third-party solutions to them. Alchemy's taxonomy, data products, and MCP server, which now reaches more than three times as many users as its web interface, provide the structured GTM foundation and downstream integrations that the SKV experience builds on.
This is a high-impact, AI-first leadership role where you will architect agentic systems, ship production GenAI features, and lead a team solving problems at the intersection of knowledge management, agentic authoring, multi-agent orchestration, retrieval-augmented generation, and closed-loop measurement, all at AWS scale, using the very AI services AWS builds for its customers.
Key job responsibilities
- Lead the engineering team that owns GTM playbooks and the recommendations engine within the Specialist Knowledge Vault, along with the Alchemy platform and its integrations.
- Architect and ship production agentic AI systems using LLMs, RAG, MCP, and multi-agent orchestration, delivering endorsed sales plays, assets, and next-best actions to field teams in the tools where they already work.
- Build, manage, and develop a high-performing SDE team, maintaining Amazon's hiring bar while fostering a culture of technical ownership, rapid experimentation, and operational excellence.
- Drive the move of GTM authoring from hand-maintained sales plays to agent-assisted playbooks in KV, with versioning, pre-publish testing, conflict detection, and health scoring that give field teams confidence in AI output.
- Evolve the recommendations engine from identifying knowledge gaps and inaccurate content to recommending and drafting the next best action, using signals such as agent conversations, field adoption, pipeline, and customer feedback.
- Own the technical strategy and roadmap across all products, making trade-offs between speed-to-delivery and long-term platform scalability, in close partnership with the engineering teams that share the KV platform.
- Partner with Product, Science, GTM, TFC, and field leadership to translate ambiguous problems into engineering deliverables, and represent the team in executive reviews with Directors and VPs.
About the team
You will be joining a high-growth engineering organization that is at the forefront of applying generative AI and agentic technologies to transform how AWS GTM teams plan, author, and deliver strategy to the field. The pace of innovation is high, the problems are ambiguous, and the impact is measured by how quickly customer opportunities become sales plays in the hands of thousands of field team members and the pipeline they create. This role offers the opportunity to shape how an entire organization builds, governs, and measures AWS' go-to-market content.
About AWS
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
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.
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 in the cloud.BASIC QUALIFICATIONS
- 3+ years of engineering team management experience
- 7+ years of engineering experience
- 6+ years of leading the definition and development of multi tier web services experience
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
- Knowledge of engineering practices and patterns for the full software/hardware/networks development life cycle, including coding standards, code reviews, source control management, build processes, testing, certification, and livesite operations
PREFERRED QUALIFICATIONS
- Experience in communicating with users, other technical teams, and senior leadership to collect requirements, describe software product features, technical designs, and product strategy
- Experience in recruiting, hiring, mentoring/coaching and managing teams of Software Engineers to improve their skills, and make them more effective, product software engineers
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, NJ, Jersey City - 203,400.00 - 275,200.00 USD annually
USA, NY, New York - 203,400.00 - 275,200.00 USD annually
USA, TX, Austin - 184,900.00 - 250,200.00 USD annually
USA, TX, Houston - 184,900.00 - 250,200.00 USD annually
USA, WA, Seattle - 184,900.00 - 250,200.00 USD annually
Salary Context
This $184K-$275K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $194K across 123 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 9,079 AI roles we're tracking, AI Product Manager positions make up 5% 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 947 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $184K to $275K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Amazon Web Services AI Hiring
Amazon Web Services has 64 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Scientist, AI Software Engineer. Positions span New York, NY, US, San Francisco, CA, US, Cupertino, CA, US. Compensation range: $160K - $299K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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