2 of 2 Permanent Memory Management Jobs in Tyne and Wear

Engineer programme. Key Responsibilities Design and build production‐grade agentic solutions end‐to‐end: multi‐agent orchestration, RAG pipelines, policy‐based routing, tool invocation, memory management, and lifecycle observability Build and own RAG pipelines: embeddings, chunking strategy, vector search, context window engineering and tuning against real quality targets … Integrate and abstract across multiple LLM providers — OpenAI, Anthropic, Vertex AI, and open‐source models — with fallback routing, token, cost, and latency management Implement LLMOps in production: eval harnesses with real quality metrics, prompt versioning, observability tooling (LangSmith, Braintrust, or equivalent), cost and safety monitoring Embed directly with client ...

programme. Key Responsibilities Architect and govern production-grade agentic solutions at enterprise scale: multi-agent orchestration across complex environments, RAG pipelines, policy-based routing, memory management, and programme-level lifecycle observability Define RAG pipeline standards across engagements: establish chunking and embedding strategies, set quality benchmarks, and ensure metric … AutoGen, or equivalent — at production depth, not tutorial level Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management, latency and cost tradeoffs RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering LLMOps fundamentals: eval harness design, prompt versioning, and production ...