- Own AI product builds end to end, from concept through production, on a client engagement
- Decide what to build and how: gather requirements, pressure-test what stakeholders ask for, and prioritize the work that matters
- Prototype fast with LLMs, then harden into production with the data pipelines, integrations, and evals that make it trustworthy
- Do the data engineering enterprise work requires: ingestion, data modeling, and ETL
- Serve as the primary technical contact for clients - talk shop with their engineers and give their executives clarity
- Instrument what you build (analytics, funnel metrics, error analysis) and iterate on real usage, not assumptions
- Own reliability, security, and cost: auth, secrets, PII handling, and not blowing up the cloud bill
- Write up what you learn; for the team, for clients, and publicly
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