Job Description

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.

ML/AI Engineer

Location: Bengaluru, India
Team: Business Insights & Analytics

About Lilly

Lilly unites caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana, and our employees work to discover and bring life-changing medicines to those who need them while improving the understanding and management of disease.

The Lilly Bengaluru Business Insights & Analytics team was established in 2017 to use innovative data mining and analytics to support business decisions across marketing functions in the US and international affiliates. The team has grown rapidly and now includes more than 100 professionals with capabilities spanning data management, data science, analytics, pharmaceutical commercial operations, and business insights. The team delivers analytics that support decision-making across Marketing, Sales, Medical Affairs, and other business functions, in close partnership with a parallel data and analytics team in Indianapolis.

Within this organization, the ML/AI Engineering team builds and operates the infrastructure that takes machine learning and generative AI solutions from prototype to production at scale.

This role sits at the intersection of software engineering, MLOps, and applied generative AI. You will design and build the platforms, pipelines, and applications that take machine learning and AI solutions from prototype to production at scale. This role is well suited for someone who is comfortable operating independently, contributing to technical design decisions, and building reliable production-grade systems.

Key Responsibilities

Infrastructure and Automation

• Design, build, and maintain CI/CD pipelines using GitHub Actions for ML and AI application deployments
• Contribute to deployment architecture and infrastructure design decisions for new ML/AI projects
• Deploy and orchestrate ML/AI workloads across environments using Docker, Kubernetes, and Prefect
• Apply and champion software engineering best practices including version control, code review, testing, and documentation across ML/AI systems

Generative AI and LLM Applications

• Develop and deploy production-grade applications using Claude or similar large language models
• Build agentic AI systems using frameworks such as LangGraph, including tool use, multi-step reasoning, and retrieval-augmented generation architectures
• Contribute to LLMOps practices including prompt versioning, evaluation pipelines, cost and latency monitoring, and guardrails
• Help define scalable implementation patterns for LLM-powered applications and retrieval systems
• Integrate vector databases such as Pinecone or similar platforms for semantic search and knowledge retrieval

Model Lifecycle Management

• Own operational aspects of the model lifecycle, including deployment, monitoring, retraining, and decommissioning
• Monitor production models for data drift, model drift, and performance degradation, and drive issue triage and resolution
• Contribute to and extend team MLOps frameworks to support new models and use cases

Collaboration and Communication

• Partner with data scientists, software engineers, infrastructure teams, and business stakeholders to translate requirements into scalable technical solutions
• Contribute to technical design discussions for proof-of-concept and production solutions
• Independently drive portions of technical delivery while escalating risks and dependencies appropriately
• Strengthen team standards for AI engineering through hands-on contribution and continuous improvement

Required Skills and Experience

Foundational

• Python
• Git/GitHub
• Software engineering best practices
• CI/CD fundamentals

MLOps and Deployment

• Docker
• Kubernetes
• Prefect
• Production monitoring
• MLOps pipelines
• Model versioning and lineage

Generative AI and LLMOps

• Experience with Claude or comparable large language models
• Agentic AI frameworks and design patterns
• LangGraph
• Pinecone or similar vector databases
• Retrieval-augmented generation architectures
• LLM application development
• Prompt engineering and evaluation

Cloud and Infrastructure

• AWS services such as EC2, ECS, S3, Lambda, IAM, and CloudWatch, or equivalent services

Required Qualifications

• 5-8 years of hands-on experience building and operating ML/AI pipelines in production environments
• Strong proficiency in Python, with a track record of writing clean, testable, production-quality code
• Demonstrated experience with containerization, orchestration, and CI/CD pipelines in production settings
• Working knowledge of AWS cloud services and experience designing and deploying solutions using managed services
• Experience developing or deploying LLM-based applications, including prompt engineering, retrieval-augmented generation, or agentic workflows
• Familiarity with MLOps practices including model versioning, monitoring, automated retraining, and deployment strategies
• Experience contributing to technical design decisions and translating ambiguous requirements into scalable implementations
• Ability to operate independently on well-scoped problems while collaborating effectively on larger platform and architecture decisions
• Strong verbal and written communication skills, including the ability to explain technical decisions to both technical and business audiences
• Experience working in Agile or Scrum environments

Preferred Qualifications

• Databricks
• R
• Experience with additional vector databases
• Experience with ML observability platforms
• Experience with Terraform or other infrastructure-as-code tools
• Experience with advanced GitHub Actions workflows

Education

• Bachelor's or Master's degree in Computer Science, Computer Applications, or a related technical field
• Specialization or certifications in Machine Learning, Data Science, AI Engineering, or Cloud Architecture are a plus

Lilly is an equal opportunity employer and values diversity.

Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form ( https://careers.lilly.com/us/en/workplace-accommodation) for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response.

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

#WeAreLilly