An ideal fit for this role would be someone who has experience in Data
Engineering and transitioned to design and built advanced data science and AI
solutions across the enterprise. This role operates at the intersection of data
science, data engineering and AI engineering, driving high‑impact use cases in
machine learning, and Generative AI while ensuring responsible, secure, and
scalable adoption.

This is a hands‑on individual contributor role with broad influence across
teams, platforms, and leadership stakeholders.

Key Responsibilities:

AI & Advanced Data Engineering Architecture
Lead design and adoption of AI-powered data engineering solutions leveraging
Azure AI, Snowflake Cortex AI, and contemporary LLM ecosystems
Architect and implement RAG (Retrieval-Augmented Generation) patterns,
semantic search, agent-based workflows, and intelligent data products
Define scalable patterns for LLM integration with enterprise data platforms,
including prompt orchestration, context management, and grounding strategies
Establish best practices for model evaluation,



monitoring, guardrails, and
responsible AI implementation
Drive adoption of vector-based architectures (embeddings, Vector DBs) for
enterprise AI use casesAI

Data Platform & AI Architecture Leadership
Own end-to-end data and AI platform architecture across lakehouse, warehouse,
and AI layers ensuring scalability, performance, and cost efficiency
Define standards for AI-ready data modeling, including semantic layers,
feature stores, and domain-driven data products
Architect integration between Azure AI services, Snowflake Cortex AI, and
enterprise data platforms
Drive platform optimization and enablement of real-time and batch AI
inference pipelines
Establish reusable frameworks for AI/ML lifecycle management (MLOps/LLMOps)

Enterprise Integrations & AI Data Products
Lead design of consumer-centric AI data products, enabling analytics,
applications, and AI-driven decisioning
Architect robus

📌 Sr Principal Data Scientist Hyderabad
🏢 providence
📍 Hyderabad