Description

About the job

• 15+ years of hands-on data engineering and architecture experience, with 3–5+ years building

production AI/ML and LLM-era data infrastructure.

• Proven experience designing enterprise-scale AI data platforms that serve multiple AI consumers —

not just one application or pipeline.

• Deep expertise in lakehouse and data mesh architectures: Databricks, Delta Lake, PySpark, Kafka,

Spark Structured Streaming, cloud-native data services (AWS, Azure).

• Hands-on experience with vector stores, semantic models, knowledge graphs, and retrieval

infrastructure in production environments.

• Working knowledge of LLMOps: model serving pipelines, MLflow, CI/CD for AI, automated

evaluation, and production monitoring.

• Strong background in data governance, security, and compliance in regulated industries (financial

services, payments, cybersecurity, healthcare).

• Experience defining data access controls for AI agents and automated systems — not just human

users

Key Skills

Education

Any Graduate