AI Data & Knowledge Engineer - Assistant Vice President

Overview

Lead the design and evolution of enterprise-grade data and knowledge foundations for AI systems. Define standards and architecture patterns for reliable, governed, and scalable data platforms supporting AI applications.

What you'll do

• Lead architecture and technical design for AI-ready data platforms, including ingestion, transformation, storage, serving, and retrieval layers.
• Define standards for scalable batch and streaming data pipelines, ETL/ELT frameworks, and backend data services supporting AI applications.
• Own data and knowledge architecture across the platform, including knowledge graphs, ontologies, semantic models, and retrieval architectures.
• Establish and enforce standards for data contracts, lineage, observability, governance, and quality controls for AI systems.
• Partner with platform, architecture, and AI engineering teams to design robust integration patterns across enterprise source systems and AI services.
• Drive adoption of modern data engineering practices, including orchestration, CI/CD for data pipelines, reusable framework components, and automated quality checks.
• Lead evaluation and implementation of technologies across big data processing, vector search, graph stores, metadata tooling, and AI data infrastructure.
• Coach and develop Officers and Senior Associates; support hiring and capability building across the pod.
• Represent the Data/Knowledge function in architecture reviews, governance forums, and cross-functional planning discussions.

What you'll need

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or a related field.
• 8–14 years of experience in data engineering, data architecture, platform engineering, or large-scale data systems.
• Deep expertise in building scalable data pipelines, ETL/ELT frameworks, and backend data platforms.
• Strong experience with distributed data processing technologies such as Spark, Databricks, Flink, or equivalent ecosystems.
• Experience designing enterprise data architectures across data lakes, warehouses, lakehouses, and AI-serving layers.
• Strong understanding of orchestration, observability, performance tuning, reliability engineering, and cost-aware pipeline design.
• Deep expertise in RAG, GraphRAG, knowledge graphs, ontology design, semantic modeling, and metadata-driven architectures.
• Experience owning data governance frameworks, data contracts, lineage, and compliance controls at scale.
• Proven experience leading technical teams and influencing architecture decisions across multiple domains.
• Strong written and verbal communication skills, including the ability to present technical designs and trade-offs clearly.

Nice to have

• Cloud certifications in AWS and/or Azure.

Details

• Location: Hyderabad, India.
• Work schedule: On-premise.