Job Opportunities API

12 of the top 15 pharma generic companies in the world trust Leucine to manufacture the drugs that reach millions of patients every day. Pharma manufacturing is one of the last large industries still running on paper, Excel, and tribal knowledge. A single missed step in a batch record can mean a recall, a regulatory warning, or a drug that should never have left the plant. Leucine is the AI platform replacing all of it. Series A, backed by top-tier global investors, 60+ pharma enterprises in production, and the team that's going to define this category. About the Role You will deploy and run our platform inside pharma customers with your own hands, and be the product manager for the platform based on what that teaches you. You’ll model a customer’s QC and manufacturing data, build the pipelines, ontology, and apps (directing our AI agents to do the heavy lifting — and verifying everything they produce), and get real users live. Then you’ll turn the friction and gaps you hit in the field into the product roadmap, working directly with the CPO. This is the shortest path from a customer’s real problem to a shipped platform improvement. It suits a technical builder who is genuinely comfortable in SQL and data models, skeptical and sharp when reviewing AI output, and obsessed with getting the data right. What You’ll Do Deploy the platform at customers (hands-on) - Model the customer’s data end-to-end: build source→gold data pipelines (transforms, joins, incremental loads, dedup, grain, schema drift) and design the business ontology — entities (batch records, deviations, QC results, instruments, sampling, etc.), their relationships, and how they map to the customer’s real source columns. - Write and debug SQL against the data warehouse; validate correctness with dry-runs, row counts, and spec checks before anything reaches a user. - Build operational apps and dashboards on the platform for customer users (data bindings, platform components), and drive adoption with quality/manufacturing stakeholders. - Troubleshoot pipeline, data-model, and app issues to root cause — across the data, the generated artifacts, and logs. Operate and verify AI agents - Direct the platform’s AI agents to generate pipelines, data models, and apps — and rigorously review their output: read the generated SQL/specs, catch silent failures (all-zero dashboards, fan-out joins, malformed models), and tell a genuine platform bug apart from a bad prompt. - Use AI-assisted / “vibe coding” tools to prototype and validate ideas before spending engineering time. Be the platform’s PM - Convert field learnings — recurring friction, capability gaps, real bugs — into prioritized, well-scoped product work; own the roadmap for one or more areas with the CPO. - Write crisp PRDs and specs engineering and design can build from without guesswork. - Define and track the metrics that matter (time-to-value on a deployment, adoption, retention) and report to the CPO. - Partner with engineering, design, sales, and customer success to ship and land features.