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.
Lead AI/ML Engineering - AI Data Platforms (Grade-29) - 2369759
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 healthcare innovation on a global scale. Join us to start Caring. Connecting. Growing together.
We are seeking a Lead AI/ML Engineering - AI Data Platforms to lead design, implementation, adoption, and production success of modern AI-ready data platforms supporting AI/ML, Deep Learning, Generative AI, Copilots, RAG, and Agentic AI solutions.
This role requires strong data engineering background with hands-on AI/ML engineering leadership, including experience building scalable lakehouse, data lake, data warehouse, streaming, semantic, vector, and governed data platforms that enable enterprise AI products from development through production operations.
Success will be measured through platform adoption, reuse, onboarding time, data quality, pipeline reliability, SLA adherence, cost efficiency, AI delivery acceleration, production stability, and measurable business outcomes.
Primary Responsibilities
AI Data Platform Leadership
•Lead end-to-end design, implementation, and operation of AI-ready enterprise data platforms across lakehouse, data lake, warehouse, streaming, and event-driven architectures.
•Define scalable data engineering patterns for AI workloads, including ingestion, curation, transformation, feature preparation, semantic access, and governed consumption.
•Build reusable pipelines, APIs, data products, embeddings pipelines, vector indexing, semantic retrieval, and RAG-ready data services.
•Ensure data quality, observability, metadata, lineage, privacy, security, and compliance controls across AI data assets.
AI/ML Engineering & Production Delivery
•Lead delivery of AI/ML, Deep Learning, GenAI, Copilot, RAG, and Agentic AI products from development through deployment, monitoring, and ongoing production support.
•Drive AIDLC practices across data readiness, experimentation, model development, deployment, evaluation, observability, feedback loops, and continuous improvement.
•Ensure implementation of MLOps, LLMOps, CI/CD, automated testing, release automation, model lifecycle management, and production AI operations.
•Partner with data scientists, applied scientists, ML engineers, platform teams, product, and business stakeholders to operationalize AI solutions at enterprise scale.
Platform Adoption & Success Measurement
•Own adoption of AI data platforms across AI/ML teams, product teams, engineering groups, and enterprise consumers.
•Define and track platform success metrics including usage, reuse, onboarding time, data product adoption, pipeline reliability, SLA adherence, cost efficiency, and AI delivery acceleration.
•Establish platform documentation, enablement, onboarding, support models, and developer experience practices.
•Drive continuous improvement using telemetry, operational metrics, user feedback, platform performance, and business impact measures.
RAG & Agentic AI Enablement
•Enable data foundations for LLM applications, enterprise copilots, semantic search, RAG pipelines, and agentic workflows.
•Lead implementation of embeddings pipelines, vector stores, semantic layers, context engineering, knowledge retrieval, and AI data-serving patterns.
•Ensure GenAI and Agentic AI solutions are reliable, secure, governed, cost-efficient, and aligned to enterprise architecture standards.
•Promote reusable RAG templates, prompt workflows, retrieval services, orchestration patterns, and platform accelerators.
MLOps, LLMOps & Platform Excellence
•Drive enterprise adoption of MLOps, LLMOps, and AgentOps practices.
•Establish standards for CI/CD automation, model lifecycle management, evaluation frameworks, monitoring, observability, deployment automation, and governance controls.
•Partner with platform teams to strengthen AI infrastructure and shared services.
Engineering Governance & Quality
•Define engineering standards, architecture guidelines, testing strategies, and operational readiness requirements.
•Lead technical reviews and ensure alignment with enterprise architecture principles.
•Establish quality gates for performance, reliability, scalability, security, and Responsible AI compliance.
•Drive continuous improvement through delivery metrics, performance measurements, and operational insights.
Talent & Team Leadership
•Lead and develop AI/ML engineering teams across multiple programs.
•Coach engineers and technical leads on AI engineering best practices, architecture, and delivery execution.
•Build organizational capability in AI engineering, AIDLC, automation, and emerging AI technologies.
•Foster a culture of innovation, accountability, collaboration, and engineering excellence.
Organizational Impact
•Influence enterprise AI engineering strategy and operating models.
•Drive adoption of reusable platforms, frameworks, accelerators, and delivery methodologies.
•Improve engineering productivity through automation, standardization, and platform-led approaches.
•Support organizational AI transformation and capability building initiatives.
Required Qualifications
•Bachelor's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, Information Technology, or related field; master's degree preferred.
•15+ years of experience in data engineering, AI/ML engineering, software engineering, or platform engineering.
•5+ years of experience leading engineering teams and large-scale enterprise technology delivery programs.
•Strong hands-on background designing and implementing enterprise data platforms for AI/ML, Deep Learning, GenAI, RAG, Copilot, and Agentic AI workloads.
•Experience building AI-ready data capabilities including batch/streaming pipelines, ETL/ELT, feature engineering, semantic layers, metadata, data quality, lineage, and governed data access.
•Proven experience delivering AI/ML products and platforms from development through production deployment, monitoring, maintenance, and scaling.
•Experience with MLOps, LLMOps, AIDLC, CI/CD, model lifecycle management, observability, and production AI operations.
•Strong programming and data engineering skills using Python, SQL, Spark, PySpark, and modern cloud data engineering frameworks.
•Experience with Azure, AWS, and/or GCP data and AI platforms, distributed processing, APIs, microservices, orchestration, and production engineering practices.
•Strong stakeholder management, delivery leadership, communication, and problem-solving skills.
