AI Engineer, BI&A Data Science & AI

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 EngineerLocation: Bengaluru, IndiaTeam: Business Insights & AnalyticsAbout LillyLilly 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.Role OverviewThis 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 ResponsibilitiesInfrastructure and AutomationDesign, build, and maintain CI/CD pipelines using GitHub Actions for ML and AI application deploymentsContribute to deployment architecture and infrastructure design decisions for new ML/AI projectsDeploy and orchestrate ML/AI workloads across environments using Docker, Kubernetes, and PrefectApply and champion software engineering best practices including version control, code review, testing, and documentation across ML/AI systemsGenerative AI and LLM ApplicationsDevelop and deploy production-grade applications using Claude or similar large language modelsBuild agentic AI systems using frameworks such as LangGraph, including tool use, multi-step reasoning, and retrieval-augmented generation architecturesContribute to LLMOps practices including prompt versioning, evaluation pipelines, cost and latency monitoring, and guardrailsHelp define scalable implementation patterns for LLM-powered applications and retrieval systemsIntegrate vector databases such as Pinecone or similar platforms for semantic search and knowledge retrievalModel Lifecycle ManagementOwn operational aspects of the model lifecycle, including deployment, monitoring, retraining, and decommissioningMonitor production models for data drift, model drift, and performance degradation, and drive issue triage and resolutionContribute to and extend team MLOps frameworks to support new models and use casesCollaboration and CommunicationPartner with data scientists, software engineers, infrastructure teams, and business stakeholders to translate requirements into scalable technical solutionsContribute to technical design discussions for proof-of-concept and production solutionsIndependently drive portions of technical delivery while escalating risks and dependencies appropriatelyStrengthen team standards for AI engineering through hands-on contribution and continuous improvementRequired Skills and ExperienceFoundationalPythonGit/GitHubSoftware engineering best practicesCI/CD fundamentalsMLOps and DeploymentDockerKubernetesPrefectProduction monitoringMLOps pipelinesModel versioning and lineageGenerative AI and LLMOpsExperience with Claude or comparable large language modelsAgentic AI frameworks and design patternsLangGraphPinecone or similar vector databasesRetrieval-augmented generation architecturesLLM application developmentPrompt engineering and evaluationCloud and InfrastructureAWS services such as EC2, ECS, S3, Lambda, IAM, and CloudWatch, or equivalent servicesRequired Qualifications5-8 years of hands-on experience building and operating ML/AI pipelines in production environmentsStrong proficiency in Python, with a track record of writing clean, testable, production-quality codeDemonstrated experience with containerization, orchestration, and CI/CD pipelines in production settingsWorking knowledge of AWS cloud services and experience designing and deploying solutions using managed servicesExperience developing or deploying LLM-based applications, including prompt engineering, retrieval-augmented generation, or agentic workflowsFamiliarity with MLOps practices including model versioning, monitoring, automated retraining, and deployment strategiesExperience contributing to technical design decisions and translating ambiguous requirements into scalable implementationsAbility to operate independently on well-scoped problems while collaborating effectively on larger platform and architecture decisionsStrong verbal and written communication skills, including the ability to explain technical decisions to both technical and business audiencesExperience working in Agile or Scrum environmentsPreferred QualificationsDatabricksRExperience with additional vector databasesExperience with ML observability platformsExperience with Terraform or ot