Sr. Principal Data Scientist

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