Senior AI TPM — Forward-Deployed, Data-Driven Leader

Amazon

Nashville (TN)

On-site

USD 141,000 - 191,000

Full time

7 days ago

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Job summary

Amazon's Forward Deployed Engineering (FDE) team within Business Innovation & AI (BIA) seeks a Senior Technical Program Manager to build AI-powered, agentic solutions embedded with operating teams. You will own data and data-engineering strategy, pipelines, and architecture for AI work across GOS.

You'll ship prototypes to production, mentor engineers, and drive multi-quarter programs while reducing manual processes through AI-driven workflows.

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.

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.
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, 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.
Define the reusable patterns and standards that raise the bar for the whole team, and mentor engineers on them