Director AI or ML Engineering
UnitedHealth Group
- Location
- Gurgaon, Haryana
- Work model
- On-Site
- Level
- Staff
Skills
About this role
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. As the Director of AI/ML Engineering within Optum Rx Technology, you will lead an engineering organization responsible for architecting, building, and scaling artificial intelligence and machine learning solutions that transform pharmacy services and healthcare delivery. In this executive leadership role, you will drive strategic technical direction, oversee the end-to-end lifecycle of enterprise AI/ML solutions, and embed intelligent capabilities into core pharmacy platforms. You will champion technical innovation and ethical AI practices while empowering engineering teams to solve complex business and operational challenges.
Primary Responsibilities
Strategically use AI to solve problems, unlock opportunities, and deliver tangible business value across pharmacy benefit and technology initiatives Champion AI as a core driver of team success and proactively shape how AI technologies are developed, deployed, and utilized across engineering groups Integrate enterprise AI tools, set AI goals in development plans, and align hiring with key technical competencies, actively removing barriers to accelerate adoption Champion the ethical use of AI by embedding transparency, fairness, privacy, and accountability throughout the entire AI lifecycle Direct the architectural vision, engineering standards, and MLOps infrastructure for scalable, high-availability AI/ML models in production Partner with business, product, and data leaders to identify high-impact AI opportunities and translate strategic enterprise goals into technical roadmaps Lead, mentor, and grow a team of AI/ML engineers and technical leads, fostering a culture of technical excellence and continuous learning Establish robust operational monitoring, model governance, data security, and performance standards across all intelligent platform services Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so Required Qualifications: Bachelor's degree Computer Science, Software engineering, Data engineering, AI/ML experience (in addition to the required experience years listed below) 12+ years of experience in software engineering, with significant focus on AI/ML and data-driven systems 5+ years of experience designing and deploying cloud-native solutions (Azure or GCP), including scalable AI/ML workloads 4+ years of experience leading large-scale AI/ML engineering organizations (20-50+ team members) with globally distributed teams 3+ years of hands-on experience with Python and modern ML/GenAI frameworks Experience implementing AI-enabled software development lifecycle practices (AIDLC) Demonstrated ability to connect AI investments to measurable business outcomes (ROI, efficiency, growth, risk reduction) Preferred Qualifications: Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related quantitative field Hands-on