Principal TPM, AI Engineering, AI Engineering

Description

Software engineering is where AI is changing work fastest. Developers everywhere now use AI coding tools, but the organizational gains have not followed, and most enterprises have faster individual developers alongside the same delivery outcomes. AWS AI Engineering exists to close that gap. We help customers systematically build better software with AI across the whole software development lifecycle. AI-native development is now moving from early adopters into the enterprise, and the hard part is no longer proving that it works. It is making it scale.
We are looking for a Principal TPM to help shape how that happens. You will work alongside our engineering teams to bring direction together from two streams of signal: what we learn inside customers' engineering organizations, and where the industry is heading. Customer signal tells you what hurts today. Industry signal tells you what will be table stakes in six months. Connecting the two into a point of view the team can act on, and keeping it current as both move, is the center of this role.
Our current work includes an open-source, AI-native development lifecycle we created and continue to evolve with customers, along with offerings that give AI agents a deep understanding of large codebases and move customers toward autonomous software delivery. Those offerings will keep changing as the technology does, and the charter behind them will not.
Key job responsibilities
Bring the direction together
Work with our engineering teams to connect customer signal and industry signal into a clear direction for how AWS helps customers become AI-native across the software development lifecycle. Find where teams are solving the same problem in parallel and bring that work together, so the organization makes one piece of real progress rather than three partial ones. Help sequence work against customer readiness and keep the portfolio coherent as individual offerings come and go.
Define the methodology, then make it adoptable
Drive the definition, standardization, and adoption of our AI-native development lifecycle as something customers can run themselves. Build consensus on the practices and standards that make it real, and replace fragmented experimentation with a credible default path that fits each customer's risk tolerance.
Own adoption, and scale it
Treat adoption as the deliverable rather than the follow-up, through enablement, documentation, reference implementations, assessments, and self-service paths that carry customers from first experiment to production-scale practice. Design the mechanisms that convert individual developer adoption into enterprise standardization and that let the wider AWS field carry this work without you in the room. Account for organizations whose constraints differ from the ones you designed for, and for the long tail where adoption usually stalls.
Define how success is measured, and hold us to it
Define and track outcome-based KPIs for the charter, including adoption and depth of use inside customer organizations, persistence of changed behavior long after the engagement, and improvements in delivery speed, quality, and the economics of building software. Insist on rigorous evaluation of the offerings we build, the agents inside them, and our own claims about what works. Build measurement frameworks customers can run on themselves, and recommend stopping work that is not paying off.
Build leverage out of engagements
Turn lessons from individual customer engagements into repeatable patterns and scalable assets. Productize what was built for one customer when it has broader value, and resist treating every new request as bespoke. Represent the voice of the customer internally, translating patterns and adoption blockers into actionable input for AWS service teams so that what we learn in the field shapes the products customers build on.
Lead across the organization and with customers
Influence without relying on authority, aligning engineering, field, and regional teams worldwide on shared direction and sequencing. Act as a trusted advisor to customer executives and practitioners, solving the business problem rather than only the technical one and investing in the long-term relationship over the one-off win. Build the playbook and enablement that lets the wider AWS field carry this work, and represent it externally through open source, conference talks, and technical writing.
A day in the life
You split your time between setting direction and staying close to the work. On one day you might sit with a customer's engineering team to understand why they abandoned part of the lifecycle after week three, then meet a CTO to agree what success looks like for a 15,000 developer organization. On another you might review adoption data across regions to decide where the next investment goes, work out what a stalled rollout actually needs, or turn a pattern that worked for one customer into something the whole field can run. You make calls with incomplete information, hold direction under pressure, and change course when the evidence warrants.