3 of 3 Remote/Hybrid Prompt Engineering Jobs in the City of London

Engineer Required Technical Skills AWS & Generative AI: - AWS Bedrock experience (model selection, deployment, prompt engineering) - Agentic workflow experience ideally based on AWS AgentCore - Multi-agent orchestration frameworks (AWS Strands Agents, LangGraph, or similar) - Large Language Model (LLM) integration and fine-tuning - Experience with Claude, GPT-4, or similar … foundation models - Prompt engineering and chain-of-thought reasoning RAG & Knowledge Systems: - Retrieval-Augmented Generation (RAG) pipeline implementation - Vector database experience (CockroachDB, Pinecone, or similar) - Embedding model selection and optimisation - Semantic search and similarity matching - Context window management and chunking strategies MCP & Tool Integration: - Model Context Protocol ...

powered solutions for tracing, evaluation, and production monitoring. Implement robust experiment tracking, model versioning, and reproducibility practices with full audit trails. Design feature engineering approaches and contribute to feature store development. Support production models through monitoring, performance analysis, and continuous improvement. Apply responsible AI practices, including model explainability … with governance and quality controls. Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, DVC). Experience developing LLM/GenAI applications, including prompt engineering and RAG architectures. Familiarity with LLMOps tooling (LangSmith, LangChain, LangGraph). Understanding of model evaluation, validation, and production monitoring. Strong problem-solving ...

lead the design and productionisation of Generative AI and Large Language Model (LLM) applications. This role sits at the heart of an AI-focused engineering team, delivering scalable, production-grade systems using GCP and Google's AI ecosystem . You'll be a senior, hands-on engineer owning complex … including fine-tuning, vector search, and low-latency inference. Build end-to-end LLM applications , leveraging RAG (Retrieval-Augmented Generation) , agentic workflows, and prompt engineering. Implement robust evaluation frameworks to monitor LLM quality, hallucinations, token usage, and content safety. Develop and deploy autonomous or semi-autonomous agents using modern ...