Senior AI Engineer

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.

Skills

ArangoDB, Cypher, Gremlin, LangChain, LangGraph, Neo4j, Neptune, OpenAI Agents SDK, Python, agent, agents, graph