14 of 14 pgvector Jobs in London

consistent operation. Retrieval‐Augmented Generation (RAG) & Knowledge Architecture – expertise in RAG and knowledge‐grounded systems, including chunking, embeddings, vector databases (Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid search and retrieval evaluation. Familiarity with GraphRAG and agentic RAG. LLMOps, Evaluation & Responsible AI – operationalizing LLM and agentic systems at scale; evaluation harnesses, quality ...

with observability tools such as OpenTelemetry, LangSmith, Arize/Phoenix, Helicone, Portkey, or similar platforms. Experience with vector databases and search systems such as pgvector, Qdrant, Weaviate, Pinecone, OpenSearch, Elasticsearch, or Vespa. Experience with cloud platforms such as AWS, Azure, GCP, Vercel, Cloudflare, or Kubernetes-based environments. Experience with identity ...

Experience with continuous integration and deployment tools such as GitLab, GitHub, or Jenkins.Database ManagementVector 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.Version ControlProficient in Git and version control platforms ...

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 such ...

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 ...

technologies is highly desirable: Frameworks & Infrastructure: Apache Spark Apache Airflow Kafka Elasticsearch/OpenSearch Databases: PostgreSQL MongoDB Vector databases such as Qdrant, Milvus, or pgvector Python Ecosystem: Pandas Polars Engineering Skills: Experience building highly scalable backend systems. Strong understanding of data modelling and storage architecture. Deep knowledge of data processing ...

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 ...

platforms and scalable pipelines. Excellent knowledge of PostgreSQL (or similar relational databases) and NoSQL databases. Experience with vector databases such as Qdrant, Milvus or pgvector . Familiarity with technologies such as Apache Spark, Airflow, Kafka, Elasticsearch/OpenSearch, Pandas and Polars . Strong understanding of data modelling, storage architecture ...

/functions callable by AI agents (tool schemas, function calling, structured outputs). Experience with retrieval-augmented generation (RAG): vector databases (eg, 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 ...

Strong understanding of modern software development principles, clean code practices, data structures, algorithms, and distributed systems. Proficiency with Relational (preferably, PostgreSQL) and Vector (preferably, pgvector) databases. Preferred Qualifications (Bonus Points): Proficiency in additional programming languages such as Java, JavaScript/TypeScript, or Golang. Experience with specific frameworks like Spring ...

Expertise: 2+ years of hands‐on experience building and productionising LLM applications, with deep technical knowledge of vector databases (e.g., Qdrant, Weaviate, Pinecone, pgvector) and orchestration layers (e.g., LangGraph, LlamaIndex). Comfortable working with heterogeneous documents like pdfs. Experience working with re‐ranking models. Production-Grade Asynchronous Python: Strong software ...

Senior Machine Learning Engineer ££75,000 GBP Hybrid WORKING Location: Central London, Greater London - United Kingdom Type: Permanent Senior Machine Learning Engineer Location: London (Hybrid) Salary: £75,000 + Bonus + Excellent Benefits Are you ...

safely. Retrieval‐Augmented Generation (RAG) & Knowledge Systems : Proven experience designing RAG and knowledge‐grounded systems, including chunking strategies, embeddings, vector databases (Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid search, reranking, and evaluation of retrieval quality. Familiarity with advanced patterns such as GraphRAG and agentic RAG to reduce hallucination and improve factual … patterns Cloud AI platforms & model hosting: Amazon Bedrock, Azure OpenAI/AI Foundry, Google Vertex AI, Hugging Face Vector databases & retrieval: Pinecone, Weaviate, Chroma, pgvector, FAISS; embeddings, semantic and hybrid search, reranking MLOps/LLMOps & deployment: Docker, Kubernetes, FastAPI, CI/CD; observability, tracing, evaluation tooling (LangSmith, LangFuse); guardrails, prompt ...

mindset with a strong sense of ownership Excellent communication and collaboration skills Nice-to-have MongoDB and OpenSearch Vector databases (Qdrant, Weaviate, Milvus, Pinecone, pgvector) OIDC or identity infrastructure Infisical, PostHog, Oh Dear, or similar tooling AI/LLM platform experience GDPR-conscious cloud environments Enterprise security and compliance experience ...