01Overview
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 workbut its work worth doing. If youre driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us.
About The Role
We are looking for a very hands-on Advisor-level AI/ML Engineer to join the LillyUSA Commercial Technology team in Bengaluru. In this role, you define enterprise AI blueprints, drive frontier AI innovation, and embed intelligent capabilities into Lilly's products, operations, and decision-making. You will mentor engineers, represent our AI capabilities across the organization, and bridge cutting-edge AI research with measurable business outcomes.
What You'll Be Doing
Architecture & Governance
Define enterprise AI blueprints, platform standards, and governance frameworks for LillyUSA Commercial Technology.
Establish engineering guardrails covering model explainability, bias mitigation, audit trails, and responsible AI compliance.
Evaluate and onboard AI/ML tooling aligned to Lilly's approved stack: AWS, Azure, Databricks, CATS, EDB, and AWB.
Frontier AI & Applied Research
Lead applied development in multi-agent systems, autonomous orchestration, and LLM-based solutions for commercial use cases.
Design and deploy RAG architectures, fine-tuned models, and embedding-based retrieval systems at enterprise scale.
Assess emerging AI research and translate relevant advances into Lilly-applicable innovations.
MLOps & Production Engineering
Architect end-to-end MLOps pipelines: feature engineering, training, evaluation, deployment, monitoring, and retraining.
Set CI/CD standards for ML across CATS, EDB, AWB, Azure, and AWS with automated quality gates and model governance checks.
Ensure production-grade reliability, observability, and regulatory compliance across all deployed AI/ML systems.
AI Capability Delivery
Translate commercial business needs into AI/ML solutions across use cases such as sales forecasting, HCP engagement, customer segmentation, and anomaly detection.
Partner with analytics, data engineering, and product teams to embed AI capabilities into commercial workflows.
Stakeholder Engagement
Actively promote ideas and drive decisions across multiple teams and capabilities.
Communicate AI/ML trade-offs and recommendations clearly to both technical peers and senior business leadership.
Represent the team in enterprise AI forums and governance bodies.
Mentorship & Team Growth
Coach lower-level engineers in specialized AI/ML technologies to accelerate their technical growth.
Lead design reviews and architecture discussions; contribute to internal playbooks and reusable AI frameworks.
What Success Looks Like In This Role
Delivery & Impact
Designs: Breaks down moderately complex problems and drives initiatives and solutions for increased business impact.
Knowledge Sharing
Coaches: Shares knowledge in specialized technologies to increase team members' technical growth.
Continuous Improvement
Challenges: Challenges the status quo and provides recommendations to improve processes and drive innovation.
Influence
Multiple Teams: Actively promotes ideas and impacts decisions across multiple teams and capabilities.
Basic Qualifications
Master's in Computer Science, Machine Learning, Data Science, Statistics, or a quantitative field; OR Bachelor's with 6+ years of relevant experience.
5+ years designing, engineering, and deploying ML/AI systems in production cloud environments.
Proficiency in Python (required); strong command of PySpark and SQL.
Demonstrated experience defining AI/ML architecture standards and governance at enterprise scale.
Production MLOps experience: CI/CD for ML, MLflow or equivalent model registries, monitoring, and drift detection.
Cloud platform experience on AWS, Azure, and/or Databricks.
Experience with LLMs, generative AI, and RAG architectures in production or near-production contexts.
Preferred Qualifications
Experience with multi-agent AI frameworks, autonomous orchestration, and agentic workflow design.
Hands-on work with model distillation, fine-tuning, and embedding-based retrieval (Hugging Face, LangChain, vector databases).
Background in commercial AI use cases: next-best-action, HCP targeting, churn prediction, or marketing mix modeling.
Deep experience with Databricks (Unity Catalog, Delta Lake, Feature Store, Model Serving).
Containerization and orchestration: Docker, Kubernetes, Ray.
Prior experience in pharmaceutical, life sciences, or healthcare commercial technology.
AWS Certified Machine Learning .
