Sr. Principal Data Scientist

Providence India

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 modern 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 robust ingestion and integration patterns for structured, unstructured, and streaming data supporting AI workloads
Enable seamless integration of LLM applications with enterprise systems via APIs, event-driven architectures, and knowledge layers
Ensure alignment with enterprise security, governance, and responsible AI standards (RBAC, data privacy, model safety)

Strategic Impact & Thought Leadership

Define and drive the enterprise AI + Data Engineering strategy , aligning with business and technology roadmaps
Identify and scale high-value AI use cases leveraging LLMs, RAG, and intelligent automation
Act as a trusted advisor in architecture reviews and leadership forums for AI and data platforms
Mentor teams on AI engineering best practices, emerging technologies, and platform capabilities

Required Qualifications

15+ years of experience in data engineering, AI engineering, or platform architecture within large-scale enterprise environments
Proven experience operating at a Principal / Solution Architect level , influencing cross-functional architecture decisions
Strong expertise in modern data platforms (Snowflake, Azure Data Platform, Lakehouse architectures, medallion patterns)
Hands-on experience with Azure AI services (Azure OpenAI, Cognitive Services, AI Search, ML Services) and Snowflake Cortex AI
Deep understanding of RAG architectures , including embeddings, chunking strategies, retrieval optimization, and context orchestration
Experience with Vector Databases (e.g., Postgresql, Snowflake vector capabilities)
Strong knowledge of LLM ecosystems , including prompt engineering, tuning strategies, evaluation frameworks, and cost-performance trade-offs
Experience designing LLMOps/MLOps pipelines , including model monitoring, evaluation, versioning, and governance
Proficiency in Python and AI/data engineering frameworks
Expertise in enterprise data modeling (Dimensional, Data Vault, domain-oriented design) for AI-ready data platforms
Strong understanding of data security, governance, and AI safety , including RBAC, secrets management, and compliance considerations
Experience integrating AI solutions with enterprise platforms (APIs, microservices, event-driven architectures)
Familiarity with Enterprise Infrastructure domains (ServiceNow, CMDB) is a plus
Excellent interpersonal and stakeholder communication skills, to build strong collaboration within and across teams.

Good To Have

Experience building agent-based AI systems and autonomous workflows
Exposure to multi-modal AI (text, image, structured data)
Familiarity with knowledge graphs and semantic layers for AI
Experience driving enterprise-wide AI adoption programs