17 hours ago
Bengaluru, IndiaSenior
Responsibilities
- Architect ingestion, transformation, indexing, retrieval, and knowledge-enrichment pipelines.
- Build production RAG systems using structured, unstructured, graph, and metadata-driven retrieval.
- Define document parsing, chunking, taxonomy, ontology, entity resolution, and lineage strategies.
- Implement access-aware retrieval, freshness controls, quality checks, and auditability.
- Optimize retrieval quality, latency, scale, and cost.
- Partner with AI engineers on context construction and grounding.
- Mentor engineers and define reusable knowledge-engineering standards.
Requirements
- At least 5 years of experience is required.
- Must have data engineering skills.
- Must have built and operated production data, search, knowledge, or retrieval platforms; basic vector-database prototypes alone are insufficient.
- Advanced proficiency with Databricks Assistant, GitHub Copilot or Cursor, LlamaIndex, LangChain, Neo4j, Pinecone, Weaviate, Elasticsearch/OpenSearch, and cloud vector-search services.
- Proficiency with Python, SQL, distributed processing, orchestration, vector search, hybrid retrieval, knowledge graphs, metadata, semantic modeling, document pipelines, search, data quality, lineage, access control, cloud, APIs, CI/CD, observability, and performance engineering.
- Requires 15 years of full-time education.
Categories
AI ApplicationsData Engineering
About Accenture
Accenture is a global professional services firm providing management consulting, systems integration and technology, cybersecurity, and business process outsourcing for enterprises and governments. It operates a services-driven model delivering projects and managed services, often with major cloud and software partners, across industries worldwide. Headquartered in Dublin and publicly traded on the NYSE (ACN), it originated as Andersen Consulting and adopted the Accenture name in 2001.