Chennai, Tamil Nadu
Job Summary
Role Overview The Data SME (Senior Leadership Role) is responsible for defining and executing enterprise-wide data strategy, architecture, governance, and analytics modernization initiatives. This role requires deep expertise across data platforms, engineering, cloud ecosystems, governance, AI/ML enablement, LLMOps, and Agentic AI ecosystems, with strong leadership to drive transformation at scale. Key Responsibilities Enterprise Data & AI Strategy Define the enterprise data architecture, reference models, and technology roadmap. Establish strategy for enterprise adoption of LLMs, RAG architectures, LLMOps pipelines, and autonomous agent-based AI systems. Drive integration of structured, semi‑-structured, and unstructured data for generative AI use cases. Data Platform & Pipeline Architecture Design and govern data lake, data warehouse, and lakehouse architectures. Lead ingestion, transformation, quality, metadata, and governance frameworks. Architect real-time, batch, and streaming pipelines across cloud platforms. Implement scalable vector databases, embedding pipelines, and semantic search workloads. Cloud Modernization & Data Engineering Drive cloud data modernization using AWS, Azure, or GCP native services. Lead data engineering using Spark, Databricks, Snowflake, BigQuery, or Synapse. Implement DataOps/MLops pipelines using Airflow, ADF, Glue, or similar. Extend MLOps to LLMOps: prompt management, model registries for LLMs, evaluation frameworks, guardrails, and observability. Governance, Quality & Compliance Ensure data governance maturity—cataloging, classification, lineage, ownership, and policy automation. Establish governance for generative AI: responsible AI controls, toxicity filtering, guardrails, hallucination evaluation, and bias mitigation. Ensure compliance with GDPR, DPDP, HIPAA, PCI, SOC2, and emerging AI regulations. AI, ML, and Agentic Workflows Partner with AI/ML teams to build feature stores, training pipelines, and model deployment workflows. Enable RAG (Retrieval Augmented Generation) architectures for generative AI. Lead implementation of Agentic AI systems—tool‑-using autonomous agents, orchestrators, and workflow automation frameworks. Drive integration of enterprise systems (ERP, CRM, ITSM) with AI agents to enable autonomous decision-making and task execution. Operational Excellence & Performance Lead data platform performance, cost optimization, and operational reliability. Drive observability and monitoring across data, ML, LLM, and agentic systems. Build reusable acce
Key Responsibilities
1. To assess the domain IT landscape assessment and Application portfolio optimization for gap analysis
2. Creation of solution and architectural views (logical| conceptual| development| execution| infrastructure & operations architecture)
3. To collaborate with business and technical stakeholders, including leaders, project managers, and development teams, to understand and prioritize requirements while defining the architecture.
4. To ensure knowledge up-gradation and work with new and emerging products/technologies
5. To drive innovation by exploring and recommending new solutions within the organization.
6. To contribute towards white/technical papers and knowledge base
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