Agentic Design & Orchestration: Build agentic workflows and orchestration patterns (dynamic routing, tool-using agents, feedback loops) - contributing to the design and owning the implementation of well-scoped components.
Evals & Measurement: Own the eval harness for the features you ship. Build the datasets, the automated grading, and the offline regression suites that tell us whether a prompt or model change actually made things better. Raise the bar for evaluation across the team - judge design, offline and online metrics, and the judgment to know when a number is real. Turn production failures into evals that catch that class of failure next time.
Infrastructure for AI: Own work with SRE and build the infrastructure your features depend on, as code. Build the deployment path for AI workloads on EKS alongside our SRE and platform engineers.
Reliability, Cost & Safety: Keep our AI layer trustworthy as it grows. Instrument token spend, latency, and failure rates as first-class metrics. Design for the failure modes that matter - hallucination, timeout, rate limit, cost blowout - and make sure the system degrades gracefully instead of falling over. Respond when things behave unexpectedly in production.
Prompts & Model Configuration as Code: Treat prompts as versioned, reviewable, rollback-able artifacts rather than strings someone edited in a console. Own how we move a prompt or model change from idea to production safely, and how we back it out when it turns out worse.
Data Residency & Trust: We run regional deployments because our customers require it. Help make sure AI features respect those boundaries - that customer data stays in-region, model invocations are logged, and what we send to a model is what we intended to send. Think about prompt injection and PII exposure before an auditor does.
Execution & Experimentation: Take a defined problem and run with it. Contribute to our quarterly AI roadmap, run experiments to benchmark model value, and iterate systematically on prompts, retrieval, and model configurations based on what the data tells you.
Cross-Functional Partnership: Work closely with Product Management to turn product ideas into concrete technical work - both customer-facing features and internal workflow automation.
Grow With the Team: Participate actively in design discussions and code reviews. Ask questions early, escalate blockers, and share what you learn. Contribute to documentation practices (agents, markdown, Knowledge Bases) as we figure them out together.
Cultural Stewardship: Be a full participant in helping the engineering culture evolve as we grow.