Tech Lead sub-area · AI Engineer
How a Tech Lead guides production AI engineering
A strong Tech Lead treats AI as an engineering capability: useful only when quality, safety, cost, latency, and operations are measurable.
Responsibilities
What the Tech Lead needs to own.
- Select problems where probabilistic systems create clear user or engineering value.
- Define evaluation datasets, quality thresholds, fallbacks, observability, and human review.
- Protect sensitive data and review prompt injection, model access, retention, and supply-chain risks.
- Design for model changes, cost controls, rate limits, caching, and graceful degradation.
Strong signals
What good looks like.
- Starts with evaluation and workflow design rather than a model demo.
- Separates deterministic business rules from probabilistic model behavior.
- Makes AI output traceable, reviewable, and safe to reject.
Evidence from Sanjay's work
Principles backed by delivery.
Proposed an AI-powered clinical template builder and aligned 4 product managers from idea to execution.
Built developer-productivity tools and coached engineers on practical AI adoption.
Mentored and judged AI project ideas from 12 OraHacks teams.
