LeadDataEngineerwithAIexperience

## Accountabilities Data Pipeline Engineering: Build, optimize, and maintain robust batch and streaming data pipelines using modern cloud-native tools such as Snowflake, PySpark, Delta Lake, and Kafka, ensuring reliability, scalability, and performance. RAG & Retrieval Infrastructure: Design and implement end-to-end retrieval systems including embedding pipelines, vector databases, hybrid search, chunking strategies, and ranking mechanisms to optimize AI context relevance. Semantic & Knowledge Layer Development: Develop ontologies, entity mappings, and knowledge graphs while maintaining semantic contracts, metadata systems, and lineage tracking for AI and ML use cases. ML/LLMOps Enablement: Support ML and LLM lifecycle workflows including dataset curation, feature engineering, model evaluation, experiment tracking, and production monitoring. Agentic Data Systems: Build APIs, context stores, and tool interfaces that enable autonomous agents, including observability for reasoning traces, tool calls, and contextual outputs. Governance & Data Quality: Implement robust data governance frameworks including RBAC, PII handling, schema validation, data quality monitoring, and compliance-ready audit logging systems. Requirements This role requires a highly experienced data engineering professional with strong cloud, distributed systems, and AI infrastructure expertise. The ideal candidate combines deep technical execution with architectural thinking and hands-on experience building production-grade AI-enabled data systems. 7+ years of experience in data engineering with strong exposure to cloud-based data platforms. 2+ years of experience building production AI/ML or LLM-related data infrastructure at scale. Strong expertise in Python, SQL, PySpark, Snowflake, Delta Lake, Kafka, and Spark Structured Streaming. Hands-on experience with vector databases, embedding pipelines, and retrieval systems in production RAG environments. Solid understanding of MLOps practices including M