1 to 25 of 30 pgvector Jobs in London

Experience with continuous integration and deployment tools such as GitLab, GitHub, or Jenkins. Vector Databases: Experience with and (but not limited to) ChromaDB, Pinecone, PGVector, MongoDB , Qdrant etc. NoSQL: Familiarity with NoSQL databases (e.g., MongoDB preferred). SQL: Experience working with SQL databases like PostgreSQL. Proficient in Git and version ...

similar.* CI/CD: Comfortable owning pipelines end to end (GitHub Actions, GitLab CI, Jenkins).* Database Management: Experience with vector databases (ChromaDB, Pinecone, PGVector, Qdrant, MongoDB Vector Search); familiarity with NoSQL (MongoDB preferred) and SQL (PostgreSQL) databases.**Commercial and Stakeholder Skills*** Commercial awareness: able to reason about effort, scope ...

experimental projectsStrong Docker, Kubernetes/OpenShift, CI/CD and pytest knowledgeExperience working with large codebases and improving existing services safelyKnowledge of Azure, RAG, pgvector, Chroma, FAISS, Entra ID, OAuth/OIDC, Redis, Terraform, Prometheus or OpenTelemetry would strengthen your application. Current or recently held SC clearance is required. ...

graphs or graph databases (e.g., Jena Fuseki, Neo4j, Amazon Neptune, or equivalent). Experience with vector databases and embedding pipelines (e.g., Pinecone, Weaviate, Qdrant, pgvector). Practical experience implementing RAG architectures or LLM-integrated data pipelines. Familiarity with semantic web standards—JSON-LD, RDF, OWL, or SKOS. Strong Python skills ...

LangChain, CrewAI, Agentforce or similar Foundation model providers: Claude Code/Codex/Gemini, OpenAI or AWS Bedrock Vector databases: Pinecone, Weaviate, LlamaIndex or pgvector Cloud platforms: AWS Bedrock, GCP Vertex AI or Azure AI Studio CI/CD for AI: version-controlled prompts, automated testing, deployment pipelines ...

Build high‐performance APIs and caching strategies enabling low‐latency data access for AI agents and orchestration systems. Implement vector‐based data retrieval layers (pgVector, Pinecone) and ensure efficient embedding pipelines for AI contexts. Partner with AI teams to monitor data latency, cost efficiency, and observability metrics. Collaboration & Governance Partner ...

LangChain, CrewAI, Agentforce or similar Foundation model providers: Claude Code/Codex/Gemini, OpenAI or AWS Bedrock Vector databases: Pinecone, Weaviate, LlamaIndex or pgvector Cloud platforms: AWS Bedrock, GCP Vertex AI or Azure AI Studio CI/CD for AI: version-controlled prompts, automated testing, deployment pipelines ...

LangChain, CrewAI, Agentforce or similar Foundation model providers: Claude Code/Codex/Gemini, OpenAI or AWS Bedrock Vector databases: Pinecone, Weaviate, LlamaIndex or pgvector Cloud platforms: AWS Bedrock, GCP Vertex AI or Azure AI Studio CI/CD for AI: version‐controlled prompts, automated testing, deployment pipelines ...

governance practices, ideally in a regulated environment. Preferred qualifications: Background in financial services or another regulated enterprise environment. Experience with vector databases (for example pgvector, Pinecone, Weaviate, or Chroma) and agent or orchestration frameworks (for example LangChain, LlamaIndex, or the Model Context Protocol). Experience with MLOps/LLMOps tooling ...

code RAG Recent RAG delivery: Hands-on RAG systems built and shipped recently - current work, not an old proof of concept Vector databases (e.g. pgvector, OpenSearch, Qdrant, Weaviate), embeddings, hybrid search and reranking Experience evaluating RAG systems properly (e.g. recall@k, faithfulness) Streaming platforms such as Kafka Agentic or multi ...

have experience with: Agentic AI and multi-agent systems. LangChain, LangGraph or similar orchestration frameworks. Retrieval-Augmented Generation (RAG). Vector Databases (pgvector, Pinecone, Weaviate, Milvus, etc.). Model Context Protocol (MCP). FastAPI or modern Python backend frameworks. CI/CD and MLOps practices. Building production AI systems ...

/functions callable by AI agents (tool schemas, function calling, structured outputs). Experience with retrieval-augmented generation (RAG) : vector databases (e.g., Pinecone, Weaviate, pgvector), embeddings, and chunking strategies. Working knowledge of prompt engineering and evaluation techniques for agent reliability, safety, and cost control. Familiarity with orchestrating multi-agent workflows ...

well help you with them: Experience with advanced LLM techniques: agents, tool use, and agentic workflows Experience with vector databases (Pinecone, Weaviate, pgvector) for RAG applications Experience with feature stores (Feast, AWS Feature Store) Experience with containerisation (Docker) and orchestration (Kubernetes, ECS) Familiarity with Infrastructure as Code (Terraform, CloudFormation) Experience ...

help you with them: Experience with advanced LLM techniques: agents, tool use, and agentic workflows Experience with vector databases (Pinecone, Weaviate, pgvector) for RAG applications Experience with feature stores (Feast, AWS Feature Store) Experience with containerisation (Docker) and orchestration (Kubernetes, ECS) Familiarity with Infrastructure as Code (Terraform, CloudFormation) Experience with ...

help you with them: Experience with advanced LLM techniques: agents, tool use, and agentic workflows Experience with vector databases (Pinecone, Weaviate, pgvector) for RAG applications Experience with feature stores (Feast, AWS Feature Store) Experience with containerisation (Docker) and orchestration (Kubernetes, ECS) Familiarity with Infrastructure as Code (Terraform, CloudFormation) Experience with ...

emphasis on Python development in production environments Proven expertise in building and deploying generative AI applications, leveraging LLMs, vector databases like Pinecone, Weaviate, or pgvector, and modern open-source agentic frameworks Practical experience with microservices architecture, RESTful APIs, cloud platforms such as AWS, GCP, or Azure, and containerization with Docker ...

Llama/Mistral) and orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel, or equivalent). Practical expertise in RAG design, embeddings, and vector databases (Pinecone, Weaviate, pgvector, Azure AI Search). Experience with agentic architectures, function/tool calling, and structured outputs. Solid prompt engineering, evaluation, and LLM guardrail/safety experience. ...

developers as customers and measure whether they adopt what you build. Desirable Experience: Experience with RAG architectures and vector databases (for example Pinecone, Weaviate, pgvector, Azure AI Search). Familiarity with agent and orchestration frameworks such as LangChain, LlamaIndex or the Model Context Protocol (MCP). LLM observability and evaluation ...

DOCX and HTML, including layout-aware parsing (headings, tables, multi-column pages) • RAG retrieval pipelines: Chunking strategy, embedding models, vector search (Postgres with pgvector or similar) and metadata design • Postgres: Schema design, indexing (including vector indexes) and performance at scale • Retrieval evaluation: Building labelled ground-truth datasets and measuring retrieval ...

DOCX and HTML, including layout-aware parsing (headings, tables, multi-column pages) • RAG retrieval pipelines: Chunking strategy, embedding models, vector search (Postgres with pgvector or similar) and metadata design • Postgres: Schema design, indexing (including vector indexes) and performance at scale • Retrieval evaluation: Building labelled ground-truth datasets and measuring retrieval ...

GraphDB, RDF, OWL, SPARQL AI/ML data infrastructure: vector databases and search platforms such as Pinecone, Weaviate, Milvus, Azure AI Search, OpenSearch or pgvector; feature stores such as Feast or Tecton; model lifecycle and experiment tracking tools such as MLflow Personal attributes Comfortable working in ambiguous consulting environments, shaping ...

native SDKs; sandboxing,permissioning, and human-in-the-loop design Retrieval and knowledge: RAG and Graph RAG at enterprise scale; vector and hybrid search (pgvector, OpenSearch/Elasticsearch, or dedicated stores) including chunking strategy, reranking, and distributed retrieval; knowledge graphs and entity resolution; Text-to-SQL over complex enterprise schemas ...

comfort working with AI tools and prompting. An understanding of point in time data is a strong plus Also desirable: experience with vector databases (pgvector, Pinecone, Weaviate, Qdrant or similar); machine learning experience (PyTorch, TensorFlow, XGBoost/LightGBM) for building, maintaining and extending existing models; Bloomberg API integration to Python ...

experience with LLM APIs and frameworks (e.g. OpenAI/Anthropic SDKs, LangChain, LlamaIndex, or similar) Solid understanding of RAG architectures, embeddings, and vector databases (pgvector, Pinecone, Weaviate, Qdrant, or similar) Experience building APIs with FastAPI or Django; async Python is a plus Strong PostgreSQL skills; pgvector experience is a plus ...

AI Engineer Our client, a leading global supplier for IT services, requires AI Consultant/Engineer to be based at their client's office in Cardiff/Manchester/London, UK. This is a hybrid ...