Sr TPM, Fwd Deployed Engineer , Business Innovation and AI

AI in this role

Build AI and agentic solutions embedded with operational teams while owning the team's data engineering strategy.

claudebedrockclaude-codecodexkiro

prompt-engineeringragai-agentsdata-engineeringllm-frameworkspython

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

How we rate this

Sr TPM, Fwd Deployed Engineer , Business Innovation and AI at Amazon rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Prompt EngineeringRAGAI AgentsData EngineeringLLM FrameworksPythonClaudeBedrock

Questions you could be asked

How do you structure and test a prompt to get consistent output from a language model?
How would you design a retrieval step so the model answers from real data instead of guessing?
How do you decide when an AI agent can act on its own versus asking for approval first?
Tell me about a project where data engineering was part of your work. What did you do?
Tell me about a project where llm frameworks was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Prompt Engineering, RAG, AI Agents, Data Engineering, and LLM Frameworks. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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