Senior Engineer - RAG Engineering, Assistant Manager
State Street4 days ago
Hyderābād, IndiaStaff+
Responsibilities
- Design and maintain ontologies, semantic models, taxonomies, metadata structures, entity types, relationship definitions, and validation rules.
- Build, enrich, query, and support enterprise Knowledge Graphs using graph platforms and query languages.
- Develop ingestion and transformation pipelines for structured and unstructured content from documents, databases, APIs, and approved repositories.
- Implement document parsing, chunking, metadata extraction, embeddings, vector indexing, entity and relationship extraction, entity resolution, provenance, lineage, and data-quality controls.
- Build and enhance RAG and Graph RAG pipelines using hybrid retrieval, vector and semantic search, metadata filtering, graph traversal, reranking, grounding, source attribution, and citations.
- Define retrieval evaluation, groundedness testing, hallucination-reduction measures, observability, and performance tuning.
- Develop production-quality Python APIs and microservices with automated unit and integration tests, reusable components, error handling, and documentation.
- Use Git and CI/CD practices to deploy, monitor, troubleshoot, tune, and support knowledge and retrieval services.
- Collaborate with architects, subject matter experts, data engineers, AI engineers, product owners, and application teams to deliver reusable semantic and retrieval capabilities.
- Develop or integrate single-agent and multi-agent AI solutions with enterprise APIs, tools, knowledge repositories, and workflow services.
- Support agent orchestration, tool calling, memory, planning, routing, state management, human-in-the-loop controls, guardrails, and structured evaluation.
- Contribute to containerization, serverless deployment, infrastructure as code, cloud security, responsible AI, privacy, access control, auditability, and governance.
- Participate in code reviews, Agile ceremonies, release activities, defect resolution, and production support.
Requirements
- Bachelor's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, Information Systems, or a related discipline.
- Master's degree is preferred but not required.
- 7–12 years of overall technology experience in software engineering, data engineering, AI engineering, knowledge engineering, or enterprise platform development.
- At least 3 years of hands-on experience developing or supporting enterprise data, semantic, graph, search, knowledge, or AI solutions.
- Hands-on experience designing ontologies, semantic models, metadata structures, taxonomies, entity types, and relationship definitions.
- Strong experience with an enterprise graph platform such as Neo4j, Amazon Neptune, Graph DB, or an equivalent technology.
- Proficiency in graph data modeling and query languages such as Cypher or SPARQL.
- Experience building ingestion and transformation pipelines and implementing RAG or Graph RAG solutions.
- Experience with entity extraction, entity resolution, relationship extraction, graph enrichment, provenance, lineage, data-quality controls, hybrid retrieval, reranking, evaluation, groundedness testing, hallucination reduction, and observability.
- Strong Python programming skills, including REST APIs or microservices, automated testing, error handling, and production-quality code.
- Working knowledge of Git, CI/CD pipelines, deployment, monitoring, troubleshooting, performance tuning, and production support.
- Knowledge of RDF, RDFS, OWL, SHACL, labeled property graphs, knowledge representation, and semantic validation.
- Preferred experience with AI agents, LangChain, LangGraph, LlamaIndex, Semantic Kernel, LangSmith, Eval Tools, AWS Bedrock Agents, guardrails, foundation-model integrations, cloud-native services, containerization, serverless deployment, infrastructure as code, cloud security, responsible AI, privacy, access controls, auditability, model risk, and AI governance.
- Proven experience delivering Knowledge Graph, Ontology, RAG, Graph RAG, Agentic AI, or Generative AI solutions from design through production implementation.
- Strong analytical, problem-solving, technical documentation, communication, presentation, and cross-functional collaboration skills.
Benefits
- Inclusive development opportunities and flexible work-life support.
- Paid volunteer days.
- Employee networks and connection-focused workplace programs.
Categories
About State Street
State Street is a global custodian bank and asset manager serving institutional investors with investment servicing, fund accounting, trading/FX, data, and index/active strategies. Its products include State Street Global Advisors’ funds and ETFs, the Charles River Investment Management Solution, and the front-to-back State Street Alpha platform. Founded in 1792 and headquartered in Boston, the company is publicly traded on the NYSE (ticker: STT) and operates across major markets worldwide.