Director AI/ML Engineering

Optum; Chennai District; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

Optum in Chennai seeks a senior leader to define and drive the enterprise Data AI Platform strategy, ensuring scalable, secure platforms for Analytics, ML, GenAI, and Agentic AI. You will build AI-ready data foundations, lead data engineering teams, modernize legacy systems, and partner with Data Science and AI teams to operationalize AI solutions in a globally connected health platform.

This role requires strong executive communication, budget stewardship, and a passion for responsible AI and

Qualifications

• Extensive leadership experience in Data Engineering, Data Platforms, AI/ML Platforms, or Enterprise Data Architecture.
• Experience implementing Data-as-a-Product frameworks and enterprise data strategies.
• Experience managing large-scale engineering organizations, budgets, vendors, and cloud cost optimization initiatives.
• Solid expertise in cloud-based data ecosystems and modern data platform architectures.
• Deep knowledge of data engineering concepts including data pipelines, APIs, data products, streaming, and real-time processing.
• Hands-on understanding of AI/ML operationalization, including: MLOps, GenAI, Agentic AI, Vector Databases, RAG, Embeddings, Model Deployment Monitoring, Responsible AI.
• Solid understanding of enterprise data governance, data quality, lineage, metadata management, cataloging, MDM, privacy, and regulatory compliance.
• Knowledge of emerging technologies such as: GenAI, Agentic AI, Data Fabric, Knowledge Graphs, Semantic Layers, Intelligent Automation.
• Solid stakeholder management and executive communication skills.
• Proven ability to build and lead high-performing teams across data engineering, analytics, governance, and AI domains.
• Demonstrated success in driving platform modernization, digital transformation, and enterprise-scale technology adoption.

Responsibilities

• Define and lead the enterprise Data AI Platform Strategy, ensuring scalable, secure, resilient, and cost-efficient platforms for Analytics, ML, GenAI, and Agentic AI
• Build AI-ready data foundations using semantic layers, metadata management, data lineage, knowledge graphs, and governance frameworks
• Lead data engineering teams responsible for ingestion, transformation, storage, pipelines, APIs, data products, and real-time data solutions
• Establish engineering standards, reusable frameworks, and drive modernization of legacy systems into cloud-native architectures
• Partner with Data Science and AI teams to operationalize ML, GenAI, and Agentic AI solutions
• Enable capabilities such as feature stores, vector databases, embeddings, RAG, model deployment, monitoring, observability, explainability, and Responsible AI
• Drive enterprise-wide data governance, including data quality, stewardship, cataloging, lineage, master data management, privacy, and compliance
• Govern both AI training datasets and AI-generated outputs
• Promote a Data-as-a-Product operating model with defined ownership, SLAs, and quality standards
• Prioritize platform and data initiatives based on business value, AI readiness, and measurable outcomes
• Evaluate and implement emerging technologies including GenAI, Agentic AI, Data Fabric, Semantic Layers, Knowledge Graphs, and Intelligent Automation
• Lead proofs of concept and scale successful innovations across the enterprise
• Partner with business, product, analytics, and technology leaders to align platform investments with strategic objectives
• Present technology strategy and roadmap recommendations to executive leadership
• Manage budgets, vendors, cloud spend, operational performance, and platform reliability
• Establish KPIs for performance, availability, productivity, automation, and cost optimization
• Build, mentor, and develop high-performing Data AI engineering teams
• Foster innovation, accountability, continuous learning, talent development, and career growth
• Comply with all applicable Company policies, procedures, and business directives, changes including those relating to work location, team assignments, work schedules, and flexible work arrangements

Skills

Data Engineering

Data Platforms

AI/ML Platforms

Enterprise Architecture

Leadership

Budget Management

Cloud Cost Optimization

Stakeholder Management

Governance

GenAI

Job description

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.

Primary Responsibilities

• Define and lead the enterprise Data AI Platform Strategy, ensuring scalable, secure, resilient, and cost-efficient platforms for Analytics, ML, GenAI, and Agentic AI
• Build AI-ready data foundations using semantic layers, metadata management, data lineage, knowledge graphs, and governance frameworks
• Lead data engineering teams responsible for ingestion, transformation, storage, pipelines, APIs, data products, and real-time data solutions
• Establish engineering standards, reusable frameworks, and drive modernization of legacy systems into cloud-native architectures
• Partner with Data Science and AI teams to operationalize ML, GenAI, and Agentic AI solutions
• Enable capabilities such as feature stores, vector databases, embeddings, RAG, model deployment, monitoring, observability, explainability, and Responsible AI
• Drive enterprise-wide data governance, including data quality, stewardship, cataloging, lineage, master data management, privacy, and compliance
• Govern both AI training datasets and AI-generated outputs
• Promote a Data-as-a-Product operating model with defined ownership, SLAs, and quality standards
• Prioritize platform and data initiatives based on business value, AI readiness, and measurable outcomes
• Evaluate and implement emerging technologies including GenAI, Agentic AI, Data Fabric, Semantic Layers, Knowledge Graphs, and Intelligent Automation
• Lead proofs of concept and scale successful innovations across the enterprise
• Partner with business, product, analytics, and technology leaders to align platform investments with strategic objectives
• Present technology strategy and roadmap recommendations to executive leadership
• Manage budgets, vendors, cloud spend, operational performance, and platform reliability
• Establish KPIs for performance, availability, productivity, automation, and cost optimization
• Build, mentor, and develop high-performing Data AI engineering teams
• Foster innovation, accountability, continuous learning, talent development, and career growth
• Comply with all applicable Company policies, procedures, and business directives, changes including those relating to work location, team assignments, work schedules, and flexible work arrangements

Required Qualifications

• Extensive leadership experience in Data Engineering, Data Platforms, AI/ML Platforms, or Enterprise Data Architecture
• Experience implementing Data-as-a-Product frameworks and enterprise data strategies
• Experience managing large-scale engineering organizations, budgets, vendors, and cloud cost optimization initiatives
• Solid expertise in cloud-based data ecosystems and modern data platform architectures
• Deep knowledge of data engineering concepts including data pipelines, APIs, data products, streaming, and real-time processing
• Hands-on understanding of AI/ML operationalization, including:
  • MLOps
  • GenAI
  • Agentic AI
  • Vector Databases
  • RAG (Retrieval-Augmented Generation)
  • Embeddings
  • Model Deployment Monitoring
  • Responsible AI
• Solid understanding of enterprise data governance, data quality, lineage, metadata management, cataloging, MDM, privacy, and regulatory compliance
• Knowledge of emerging technologies such as:
  • GenAI
  • Agentic AI
  • Data Fabric
  • Knowledge Graphs
  • Semantic Layers
  • Intelligent Automation
• Solid stakeholder management and executive communication skills
• Proven ability to build and lead high-performing teams across data engineering, analytics, governance, and AI domains
• Demonstrated success in driving platform modernization, digital transformation, and enterprise-scale technology adoption

Success Metrics

• Data quality and reliability
• AI adoption and business impact
• Platform availability and performance
• Data product adoption
• Engineering productivity and automation
• Cloud cost optimization
• Regulatory compliance
• Team engagement and retention

At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.