9 of 9 pgvector Jobs in the City of 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 ...

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 ...

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 ...

Eligible for DV security clearance; no sponsorship provided. Desirable: 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). Containerisation (Docker) and orchestration (Kubernetes, ECS). Infrastructure as Code (Terraform, CloudFormation). ...

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 ...

complete agent workflows , from event triggers to model endpoints to user-facing integrations. Expect to work with systems like n8n, Ray Serve, vector stores (pgvector, cloud search), and messaging surfaces such as Teams or similar platforms. Building and operating cloud infrastructure using container orchestration and infrastructure-as-code tools (e.g. ...

background and confidence using popular machine learning libraries such as PyTorch, TensorFlow, or scikit-learn Familiarity with vector-based storage and search systems (e.g. pgvector, Pinecone, Azure Search), as well as experience working with graph technologies like Neo4j Practical understanding of retrieval-augmented generation pipelines and how to implement structured ...

tools such as Dagster or Argo Putting in place standard MLOps tooling, including CI/CD and container-based workflows Working with PostgreSQL + pgvector to support vector and embedding-based use cases Building secure APIs to integrate ML services with existing enterprise systems. Future work Depending on priorities, this ...

Azure AI Foundry, OpenAI/Anthropic APIs) for model hosting, RAG orchestration, and vector search. Vector Search Strategy : Define approach for semantic retrieval using pgvector, AlloyDB AI, or Azure Cognitive Search with embeddings. Cost & Performance : Establish strategies for latency optimization and cost control across clouds. AI Enablement & Delivery RAG System … platform architecture or ML systems . Hands-on experience with GCP (BigQuery, Vertex AI) and Azure AI services . Practical knowledge of vector search (pgvector, AlloyDB AI, Azure Cognitive Search) and RAG orchestration. Strong Python or TypeScript for prototyping and integration. Familiarity with multi-cloud security and IAM patterns. ...