Data and AI Architect

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 accelerators, patterns, and platform components.

Business, Leadership & Change Management

• 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).

Required Skills & Expertise

• 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.