5 of 5 Remote/Hybrid Semantic Search Jobs in London

includes: Backend Services : Python 3.13, FastAPI, async micro-services architecture Data Layer : Neo4j graph database, PostgreSQL, complex data modelling AI/ML : OpenAI integration, semantic search, conversational agents, unstructured data analysis and extraction Infrastructure : AWS (Lambda, ECS/EKS, API Gateway, S3) DevOps : Terraform, GitHub Actions, Helm, Infrastructure … prompting, chain-of-thought, structured output, and instruction tuning Practical knowledge of RAG (Retrieval-Augmented Generation) architectures, including chunking strategies, vector embeddings, and similarity search Familiarity with LLM orchestration frameworks such as LangChain, LlamaIndex, or similar Experience designing and building agentic systems — tool-calling agents, multi-step reasoning ...

design and backend architecture Proficiency with Git Willingness to work 2 days per week onsite in London Nice to have Exposure to NLP or search systems (e.g. semantic search) Experience working with LLMs or generative AI Frontend experience (e.g. React ) Experience with Elasticsearch or OpenSearch Experience with ...

analytics environment Familiarity with tracking data (e.g., StatsBomb 360, SkillCorner, Second Spectrum) and the additional modelling challenges it introduces Experience with vector databases or semantic search systems for unstructured data retrieval Exposure to cloud infrastructure (AWS, GCP, or Azure) for scalable data pipeline deployment Experience with modern data ...

Designing and building GenAI platform components that help teams create and deploy AI solutions safely and efficiently Implementing LLM‐based systems , including RAG, embeddings, semantic search, and agent‐based workflows Shaping how GenAI systems handle prompting, grounding, evaluation, reliability, and cost control Building and evolving MLOps pipelines , from … building production AI or ML systems , with recent focus on Generative AI/LLMs A strong understanding of RAG architectures , embeddings, vector databases, and semantic retrieval Experience with MLOps , including deployment, monitoring, evaluation, and CI/CD for AI systems A solid software‐engineering background, building scalable backend services ...

practical delivery plans. The skills & experience needed Experience building, deploying, and maintaining machine learning and generative AI solutions (e.g., LLMs, embeddings, retrieval patterns, vector search, model fine‐tuning). Strong MLOps knowledge across continuous integration, deployment, monitoring, and evaluation of AI/ML models. Experience with AI infrastructure (orchestration … pipelines, GPU compute, model hosting services) and scalable AI data structures (including vector databases/semantic search). Deep software engineering capability in .NET Core/C# and modern back-end engineering, with flexibility to work across languages such as Python, Go, or TypeScript. Strong understanding of distributed ...