About the role
Requirements:
- Required Experience: Agents in production: demonstrated experience delivering a multi-step agent that performs actions in a production system, with a clear understanding of its failure modes and the controls used to contain them.
- Memory: experience designing the memory and context strategy for a long-running agent, including retention, summarisation and pruning decisions.
- Evaluation: hands-on experience building an evaluation harness, including dataset construction, metric selection and calibration of automated judging.
- Multiple models: delivered systems on at least two model providers or families, with experience migrating a live workload between them.
- Scale: experience operating an LLM or ML system in production at significant request volume, with measurable before-and-after results.
- Degree in Computer Science, Computer Engineering, or a related field.
- Advanced proficiency in Python and modern AI/ML development practices.
- Hands-on experience with agent orchestration frameworks (LangGraph, LangChain, OpenAI Agents SDK), or with custom orchestration built on model APIs directly.
- Working knowledge of graph data modelling and a graph database (Neo4j, Neptune, ArangoDB): schema design, query (Cypher or Gremlin), and judgement on when a graph is preferable to a relational or vector store.
- Strong API and data engineering fundamentals (FastAPI, PostgreSQL, vector stores) and cloud native deployment on GCP, AWS or Azure.
- Sound judgement on where LLM-based approaches are appropriate and where deterministic methods are preferable.