At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us. About Lilly At Lilly, everything we do starts with patients. We unite caring with discovery to make life better for people around the world. Headquartered in Indianapolis, Indiana, our global team of over 50,000 employees work with urgency and purpose to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. We bring our best to this work because people depend on it. If you're driven by purpose and determined to make a meaningful difference for patients, we invite you to bring your skill and your commitment to Lilly. About Technology@Lilly At Lilly, technology is not a support function. It is how a global medicine company operates, innovates, and delivers. Lilly in Bengaluru builds the capabilities that make this possible, cloud platforms, AI systems, and automation at enterprise scale, all in service of a purpose that makes this technology work genuinely distinctive, from advancing drug discovery to enabling connected clinical trials to keeping a global medicine company running at the standard patients deserve. Role Summary The AI Operating System (AIOS) Leader owns the strategy, architecture, and delivery of Lilly's AI Operating System — a connected, four-layer ecosystem (Data, Compute, Model, and Application) built on platforms including LillyCode, LillyPod, Weights & Biases, the LLM Gateway, and BioNova. This role leads the ML Engineering and Data Engineering teams that build and operate AIOS, combining deep technical credibility with people leadership to remove friction, eliminate duplicated work, and give every data scientist and research team at Lilly a faster path from idea to impact. The AIOS Leader is accountable for advancing AIOS through its roadmap phases: from the Phase 1 foundation (LillyCode, LillyPod, the LLM Gateway, and core data infrastructure), through Phase 2 ML lifecycle maturity (production experiment tracking, model registry, and marketplace expansion), to Phase 3 R&D AI at scale (multi-modal AI and shared infrastructure enabling scientific breakthroughs). The role is the central point of accountability connecting engineering, science, and enterprise architecture, ensuring AIOS and the Enterprise Stack remain complementary rather than competing investments. Key Responsibilities • AIOS Strategy & Roadmap Ownership: Set and own the strategy, architecture, and phased roadmap for the AI Operating System — from Phase 1 Foundation through Phase 2 ML Lifecycle to Phase 3 R&D AI at Scale — ensuring each layer of the stack (Data, Compute, Model, Application) advances against committed timelines. • Team Leadership & Talent Development: Lead, mentor, and grow the ML Engineering and Data Engineering teams, including matrixed specialists embedded with science squads through the ML Engineering Chapter; build a culture of ownership, craftsmanship, and continuous learning. • AIOS Platform Ownership: Own delivery and continuous improvement of core AIOS platforms — LillyCode (AI-assisted development), LillyPod (on-prem GPU compute), the LLM Gateway (governed frontier model access), Weights & Biases (ML lifecycle management), Weka Fabric (high-performance storage), and BioNova (Scientific Desktop) — ensuring each meets scale, security, and reliability requirements. • Cross-Stack Partnership: Partner with Enterprise Stack leadership (Cortex, Landing Zone, CATS/Hangar) to ensure AIOS and the Enterprise Stack remain complementary, with models built in AIOS flowing into enterprise agentic workflows and business requirements flowing back. • Compute & Infrastructure Stewardship: Govern shared, self-service access to compute and storage (GPU scheduling via Run:ai, high-performance storage via Weka Fabric) across all Lilly scientific teams, balancing guaranteed quotas, over-quota bursting, and fair prioritization. • Science Enablement: Work directly with research and science squads (e.g., genomic AI and pharmacogenomics teams) to apply AIOS capabilities to high-value problems, translating scientific compute and data needs into platform requirements. • Governance & Security: Ensure AIOS platforms complete cybersecurity review and AI Registry registration, with data-residency safeguards, usage tracking, and cost attribution across models and compute — including in regulated (e.g., GxP) environments. • Executive Communication & Advocacy: Represent AIOS to senior leadership and cross-functional stakeholders, articulating impact (e.g., active users, tokens processed, digital worker equivalents, time-to-insight) and building the case for continued investment. Qualifications • Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field, with 12+ years of relevant experience • Proven experience owning a multi-layer AI/ML platform (data, compute, model, and application layers) in a large, matrixed enterprise • Demonstrated leadership of both ML Engineering and Data Engineering functions, including managing leads across multiple squads • Deep knowledge of GPU compute infrastructure, distributed training, and high-performance storage (e.g., GPU scheduling, parallel file systems) • Experience governing frontier model access (e.g., multi-provider LLM gateways) including usage tracking, cost attribution, and vendor-agnostic architecture • Track reco