- Enterprise architecture delivery. Demonstrated ownership of production AI or ML solutions and enterprise integrations, including architecture decisions, implementation guidance and operational outcomes.
- LLMs and traditional AI. Robust understanding of large language model behavior, prompting, context limits, embeddings, model evaluation and fine-tuning trade-offs, alongside established ML methods and their lifecycle.
- Agents and RAG. Hands-on experience implementing tool-enabled agents and RAG pipelines. Ability to explain retrieval quality, grounding, tool execution, memory boundaries, failure modes and appropriate human intervention.
- Engineering and integration. Ability to build prototypes and review production code in Python and an enterprise language such as Java or TypeScript. Strong knowledge of APIs, distributed systems, SQL/NoSQL data stores and asynchronous integration.