AI Operating System (AIOS) Leader

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 RD 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 RD 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 record of delivering ML lifecycle tooling (experiment tracking, model registry, dataset versioning) at enterprise scale
• Experience operating in regulated environments (GxP, SOX, CSV/CSA) within life sciences or another regulated industry
• Strong executive presence with experience presenting technical strategy and roadmap to senior leadership
• Experience partnering with enterprise application or agent-platform teams to ensure complementary, non-duplicative investment across stacks

Core Competencies

Platform Technical Strategy - Organizational Leadership - Cross-Functional Partnership - Compute Infrastructure Governance - AI/ML Lifecycle Expertise - Executive Communication - Talent Development - Delivery at Scale

Additional Information

Up to 10% travel, including international.

Lilly does not discriminate on the basis of age, race, colour, religion, gender, sexual orientation, gender identity, gender expression, national origin, protected veteran status, disability or any other legally protected status.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.