Data SME

• 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.
• 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.
• Drive cloud data modernization using AWS, Azure, or GCP native services.
• Implement Data Ops/MLops pipelines using Airflow, ADF, Glue, or similar.
• Extend MLOps to LLMOps
  : prompt management, model registries for LLMs, evaluation frameworks, guardrails, and observability.

• 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.

• 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.

• Lead data platform performance, cost optimization, and operational reliability.
• Drive observability and monitoring across data, ML,
  LLM
  , and agentic systems.
• Build reusable accelerators, patterns, and platform components.
• Collaborate with business, Product, and IT teams to translate requirements into enterprise‑grade AI‑-ready data solutions.
• Support RFPs, pre‑sales, estimations, and strategic client conversations.
• Mentor Data Engineers, Data Architects, Analysts, governance teams, and GenAI solution teams.
• Establish and scale a Data & AI Center of Excellence (CoE).

• 20+ years of experience in data engineering, architecture, or platform leadership.
• Deep experience with data lake, warehouse, and lakehouse designs.
• Strong expertise in AWS, Azure, or GCP data ecosystems.
• Hands‑on experience with Spark, Databricks, Snowflake, Kafka, Flink, and Airflow.
• Advanced SQL, Python, ETL/ELT design.
• Experience with data modeling, metadata, lineage, and governance frameworks.
• Knowledge of data security, IAM/RBAC/ABAC, and compliance requirements.
• Expertise in distributed compute tuning and cost governance.
• LLMOps experience including prompt engineering, evaluation pipelines, vector search, embedding models, guardrail frameworks (Azure Prompt Shields, Bedrock Guardrails), and safety monitoring.
• Experience delivering agentic AI workflows using frameworks such as Lang Graph, Auto Gen, or Azure/AWS agent services (high‑level—no proprietary detail required).

• Bachelor’s/Master’s degree in Computer Science, Engineering, Data Science, or related fields.
• Cloud certifications in AWS, Azure, or GCP.
• Experience in BFSI, Telecom, Healthcare, Retail, or regulated domains.
• Knowledge of AI/ML lifecycle, feature stores, and model monitoring.
• Experience establishing RAG pipelines
  , evaluating LLM performance, and managing LLM lifecycle.
• Ability to influence senior leadership and drive large‑scale data & AI transformation.
• Strong communication, storytelling, and stakeholder management skills.
• Experience leading multi‑team global delivery environments.
• Strategic thinker with a focus on business outcomes, quality, automation, and AI adoption.
• Ability to build organizational capability in generative AI, LLMOps, and agentic automation.

• High reliability, scalability, and governance maturity of data & AI platforms.
• Reduction in data/LLM errors, hallucinations, and risk through strong governance and observability.
• Faster delivery of AI‑powered insights and decision automation.
• Increased adoption of reusable components in data, ML,
  LLM
  , and agentic systems.
• Successful delivery of modernization and AI transformation programs.
• Capability uplift through Data & AI CoE maturity.

For more information on how we process your personal data, please refer to HCLTech’s Candidate Data Privacy Notice
.