Principal Engineer – Knowledge, Data & RAG Engineering- Assistant Vice President

Overview

Own enterprise architecture for knowledge engineering, data engineering, RAG and Graph RAG, Agentic AI, testing and governance, turning business strategy into practical architectures, standards, roadmaps and delivery controls.

What you'll do

• Own the enterprise architecture for Knowledge Engineering, Data Engineering, RAG and Graph RAG, Agentic AI, testing, and governance, aligning strategy, platforms, delivery, controls, and measurable outcomes.
• Define the enterprise vision, target architecture, standards, reusable patterns, and roadmap across data, knowledge, retrieval, AI agents, testing, and governance.
• Translate business priorities into scalable solution architectures, transition plans, investment decisions, and measurable outcomes.
• Lead knowledge and data architecture, including ontologies, semantic models, knowledge graphs, metadata, lineage, data pipelines, quality, and lifecycle management.
• Architect production-grade RAG and Graph RAG services covering ingestion, indexing, hybrid retrieval, ranking, grounding, citations, provenance, and feedback.
• Establish testing, evaluation, and release standards for data quality, retrieval, prompts.
• Embed testing, monitoring, traceability, rollback, and governance controls in delivery pipelines.
• Lead architecture reviews, align stakeholders, and guide technical teams.

What you'll need

• Strong experience in enterprise architecture, technical leadership, and working with cross-functional teams.
• Strong data engineering expertise across ETL/ELT, batch and streaming pipelines, lakehouse or warehouse platforms, data quality, and DataOps.
• Expertise in designing ontologies and semantic models and implementing end-to-end RAG and Graph RAG pipelines, including ingestion, chunking, embeddings, indexing, hybrid retrieval, semantic search, graph traversal, ranking, grounding, provenance, and evaluation.
• Strong knowledge of Responsible AI, privacy, access control, model risk, auditability, regulatory compliance, and policy enforcement.
• Experience delivering solutions in financial services or another regulated industry.

Nice to have

• Exposure to modern data, streaming, workflow, monitoring, vector database, and enterprise search tools such as Snowflake, Databricks, Kafka, dbt, or Airflow.
• Familiarity with AI platforms or frameworks such as Azure AI Foundry, Copilot Studio, Vertex AI, LangChain, LangGraph, LlamaIndex, Semantic Kernel, or MLflow.
• Awareness of Model Context Protocol, connecting AI agents with tools, access controls, monitoring, and AI security.
• Experience establishing automated testing, quality gates, monitoring, traceability, release, and rollback controls within CI/CD and MLOps/LLMOps.
• An understanding of cloud costs, capacity planning, and communicating architecture proposals to senior stakeholders.
• Relevant certifications in architecture, cloud, data, graph, testing, security, governance, or Generative AI.
• Extensive technology experience, ideally including architecture, data or knowledge engineering, software or AI engineering, testing, governance, or enterprise platforms.
• Experience providing architectural guidance and contributing to standards, roadmaps, governance, and key design decisions.
• Exposure to delivering secure, scalable, and well-governed data platforms, knowledge graphs, ontologies, RAG or Graph RAG services, Agentic AI, or Generative AI solutions.
• The ability to work with senior stakeholders and bring business, architecture, engineering, testing, cloud, security, risk, and operations teams together.

Details

• Location: Hyderabad, India.