Associate Director - AI Engineering at Eli Lilly

At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We’re looking for people who are determined to make life better for people around the world.

• We are a global healthcare leader headquartered in Indianapolis, Indiana. Lilly India Commercial function BI&A (Business Insights & Analytics) team supports business decisions for the commercial and marketing functions in the US and ex-US affiliates. The team comprises 100+ members across data management, analytics, data science, business, and commercial operations. Role Summary Lilly India Commercial function BI&A is hiring an Associate Director to lead AI Engineering & Operations, responsible for building and running production-grade AI systems across the full AI lifecycle—spanning traditional ML engineering/MLOps and agentic AI systems (LLM/agent workflows, tool use, orchestration). The leader will partner closely with onshore BI&A stakeholders and Lilly IT/platform teams to deliver scalable, reliable, secure, and compliant AI capabilities that drive measurable business impact. Key Responsibilities People & Delivery Leadership • Lead, coach, and grow a high-performing team of ML/AI engineers and operations talent; set clear expectations on engineering quality, reliability, and productivity.
• Establish best practices for execution (planning, project management, technical design reviews, documentation, and continuous improvement).
• Collaborate with data scientists, software engineers, and infrastructure teams to design optimal end-to-end pipelines and AI product delivery. AI Engineering Platforms & Reusable Capabilities • Build and operationalize reusable capabilities that enable teams to develop and deploy AI tools, assistants, and agents at scale, including standardized templates/SDKs, integration patterns, and deployment readiness.
• Enable robust RAG and agentic workflows: ingestion, retrieval, orchestration patterns, tool calling, and integration with enterprise systems.
• Establish lifecycle discipline for AI systems: versioning/registry, automated evaluations, safe rollout/rollback, and continuous improvement loops AIOps, Observability & Operational Excellence • Implement end-to-end observability for AI services (pipelines, agents, and applications): monitoring, tracing, cost/usage visibility, and operational dashboards.
• Drive incident and problem management practices (triage, postmortems, prevention automation) to improve reliability and reduce operational toil.
• Optimize for latency, quality, and cost trade-offs; define and track operational metrics aligned to business outcomes. Governance, Security & Compliance • Ensure secure, compliant AI delivery with enterprise authentication, audit trails, and “security- by-design” practices (including policy-based enforcement where required).
• Implement guardrails and responsible AI practices suitable for regulated environments, in partnership with risk/security stakeholders. Strategic Growth & Thought Leadership • Identify capability gaps and opportunities; build a roadmap to expand BI&A’s AI engineering outcomes and scale adoption.
• Provide thought leadership and run Po Cs to modernize AI operations across cloud/on-prem environments, CI/CD automation, and multi-team delivery. Minimum Qualifications (Required) • 10+ years relevant experience across software/ML engineering, MLOps, or AI operations; 3+ years leading teams and delivering across multiple stakeholders.
• Strong hands-on foundation in cloud-native engineering and DevOps/MLOps practices (containers, orchestration, CI/CD, automated testing, release discipline).
• Experience operationalizing end-to-end ML solutions (training/serving/monitoring) and modern GenAI/agent workflows (evaluation, monitoring, lifecycle management).
• Proven ability to build scalable operating models, enforce quality standards, and communicate trade-offs with senior stakeholders.
• Strong communication, collaboration, and people leadership skills Preferred Qualifications (Nice to Have) • Experience with LLM/agent observability practices (tracing across prompt → retrieval/tool calls → response) and cost controls for GenAI systems.
• Experience with policy-as-code / compliance-ready audit logging patterns in enterprise environments.
• Experience in regulated domains (healthcare/life sciences/finance) operating under audit and compliance constraints. Education • Master’s degree in Computer Science/Engineering/Mathematics or related field

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