Principal Engineer – Knowledge, Data & RAG Engineering- Assistant Vice President
State Street Corporation (company site, opens in a new tab)HyderabadOn-siteDirect career pageEst. 30–50 LPA
Posted 15h ago
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Job description
Structured overview of role & requirements
About This Role
- Lead and own enterprise architecture for Knowledge Engineering, Data Engineering, RAG, Graph RAG, Agentic AI, testing, and governance, aligning strategy and delivery with measurable outcomes.
- Define enterprise vision, target architecture, standards, reusable patterns, and roadmap across related domains including data pipelines, semantic models, knowledge graphs, retrieval, AI agents, and governance.
- Architect production-grade RAG and Graph RAG services and embed testing, monitoring, traceability, rollback, and governance controls into delivery pipelines while leading architecture reviews and stakeholder alignment.
Minimum Requirements
- Strong experience in enterprise architecture and technical leadership with cross-functional teams.
- Expertise in data engineering (ETL/ELT, batch/streaming pipelines, lakehouse or warehouse platforms), and building end-to-end RAG and Graph RAG pipelines including ontology and semantic model design.
- Knowledge of Responsible AI, privacy, access control, model risk, auditability, regulatory compliance, and policy enforcement in regulated industries (financial services or similar).
- Bachelor’s degree in Computer Science, Engineering, AI, Data Engineering, Information Systems, or related field preferred; Master’s degree is a plus; Relevant certifications are a plus. Work Experience Required: Not explicitly mentioned in the JD.
Ideal Candidate Profile
- Experienced architect with a track record of delivering secure, scalable, governed AI and data platforms integrating semantic technologies and retrieval systems in regulated environments.
- Strong capability in translating business strategy into scalable technologies while managing cross-team alignment including architecture, security, risk, testing, and governance.
- Demonstrated expertise in applying pragmatic enterprise architecture principles emphasizing secure-by-design, test-by-design, reuse, and measurable quality in AI/knowledge engineering contexts.