years in data engineering, ML engineering, or similar technical roles Strong Python skills and comfort working across complex ingestion workflows Experience managing NoSQL and vector databases at scale (MongoDB, Weaviate, Pinecone, etc.) Solid understanding of modern data pipeline tools (Airflow, Prefect, Dagster) Practical experience with LLM development, embeddings, and RAG architectures Familiarity with distributed systems and cloud platforms (AWS, GCP More ❯
Generative AI services and AI/ML patterns (RAG, MLOps). Strong database management skills. Nice-to-have: Cloud certifications (Azure/GCP). Experience with vector databases (Pinecone, Weaviate). Retail or CPG industry exposure. Why Accenture? Competitive salary and benefits (30 days’ vacation, private medical, charity leave). Work on cutting-edge AI projects for global brands. Inclusive More ❯
sources. Design RAG systems: chunking strategies, document schemas, metadata, hybrid/dense retrieval, re ranking, and grounding. Manage vector/keyword indexes (e.g., Azure AI Search, pgvector, Pinecone/Weaviate). Develop and deploy advanced NLP, information retrieval, and recommendation systems that enhance Chambers and Partners' research and product offerings, including document understanding, automatic summarisation, topic modelling, semantic search, entity … Terraform). Data & Information Management Experience with relational/semi structured database (MS SQL and Cosmos DB) and vector search indexing (Azure AI Search/pgvector/Pinecone/Weaviate/Milvus/Qdrant) plus Neo4j or equivalent graph database. Software Engineering & Architecture Solid grasp of SDLC practices: unit/integration/E2E testing, code review, documentation, and maintainable software More ❯
