Sr TPM, Fwd Deployed Engineer , Business Innovation and AI
About the role
thenwith that text. - Responsibilities: The "Key job responsibilities" bullet list. We'll extract bullet points. - Requirements: Basic Qualifications. - Qualifications: Preferred Qualifications? Could be separate but we can combine? The instruction: use headings for sections e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. So we need Requirements (basic qualifications) and Qualifications (preferred). Also Skills maybe separate but we can include relevant skills in Responsibilities or Qualifications. We'll see if there is explicit skills list: they mention experience with SQL, Python, AWS data services, etc. Those are in Basic Qualifications. Preferred includes more specific skills. We could put Skills section for technical skills like SQL, Python, AWS services, agentic harnesses, etc. But we must not create thin sections. Better to incorporate skills into Requirements/Qualifications. However we have headings for Skills; we could omit if not enough distinct content. The instruction: Prefer a few substantive sections over many thin ones. So maybe we just have About the role, Responsibilities, Requirements, Qualifications, Benefits, Pay. Schedule not present. Benefits: there is description of benefits. Pay: salary ranges. So include those. We must not include boilerplate like EEO, accommodation, etc. Also remove duplicated lines. Let's extract. About the role text: Starting after "Description". The paragraph: "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." We'll put that as
. Responsibilities: list under "Key job responsibilities". There are subheadings: Build AI & Agentic Solutions, Embedded (the primary work) with bullet points; Program Delivery; Data & Data-Engineering Strategy (the DE edge); Raising the Bar. Also "A day in the life" maybe not responsibilities but could be included. We'll treat responsibilities as the bullet points under those headings. Let's extract bullet points: Under Build AI & Agentic Solutions, Embedded: - 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: maybe not responsibilities but could be included as a narrative; but we could skip to avoid thin section. However it's descriptive of typical day; could be part of About the role? Might be okay to include as a paragraph under About the role? But we already have About the role description; we could add a separate paragraph for "A day in the life". However instruction: only include sections when posting has real specific content. It does have content; but we could incorporate into About the role or Responsibilities. To keep sections few, we could add a short paragraph under About the role after the initial description. But we must not invent content. We'll keep it as part of About the role maybe as a second paragraph. Now Requirements (Basic Qualifications): List: - 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 We'll put each as
- .
Qualifications (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
Benefits: The paragraph after "Amazon also offers comprehensive benefits including ..." includes health insurance, 401(k) matching, paid time off, parental leave. Also earlier: "Your Amazon package will include sign-on payments and restricted stock units (RSUs)." Also mention "Learn more about our benefits at https://amazon.jobs/en/benefits." We'll include substantive content: sign-on payments, RSUs, health insurance (health insurance details), 401(k) matching, paid time off, parental leave. We'll not include the link maybe but could keep as part of description. We'll keep as
- 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
- 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
- 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,
. Pay: The base salary range listed: "USA, TN, Nashville - 141,300.00 - 191,100.00 USD annually" and "USA, WA, Bellevue - 148,700.00 - 201,200.00 USD annually". Also mention "Final compensation will be determined based on factors including experience, qualifications, and location." We'll include that. Schedule: Not present, so omit. Now produce HTML fragment:
About the role
... maybe two paragraphs.
etc. Make sure not to include any extra text outside HTML. Let's craft. We need to escape any HTML special characters? The text includes ampersands etc. We'll keep as is; but ensure no stray < or > inside text that could be interpreted as tags. There are parentheses and hyphens fine. There's "AI-powered". No '<' or '>'. We'll produce:About the role
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.
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.
Then Responsibilities: