Roles, skills, and what to learn.

Ships features end-to-end: frontend UI, backend APIs, databases, and the glue between them.

Sets the structural decisions: system boundaries, APIs, data models, and the trade-offs that shape how a codebase scales.

Owns the path from commit to production: CI/CD, containers, infrastructure-as-code, observability, and reliability.

Builds and maintains the pipelines that move, transform, and store data at scale — the plumbing behind every data product.

Trains, evaluates, and productionizes ML models: from feature engineering through serving and monitoring in production.

Designs and operates defenses that protect systems, data, and users — combining threat modeling, network security, and compliance.

Owns the why and the what of a product: discovery, prioritization, roadmaps, and the bridge between users, business, and engineering.

Builds applications powered by ML and LLMs: prompt design, RAG systems, model APIs, and the data pipelines that feed them.

Bridges ML research and production: experiment tracking, model versioning, automated retraining pipelines, and reliable inference infrastructure.

Builds the internal developer platform that makes every other team faster: golden paths, self-service infrastructure, and the golden ratio of abstraction.

Evaluates, red-teams, and governs AI systems to ensure they are reliable, fair, and safe — the technical side of responsible AI deployment.