Advisor - ML Engineering & Operations
Eli Lilly and CompanyMatch Score
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Job Description
Structured overview of role & requirementsAbout This Role
Own end-to-end machine learning architecture, feature engineering, pipeline design, deployment, and production monitoring for key commercial analytics initiatives.
Lead technical integration and serve as primary liaison with Lilly's Agentic AI engineering team, defining APIs, SLAs, and architectures to adopt agentic AI capabilities.
Provide technical oversight and coordination for multiple projects and junior engineers, ensuring best practices in CI/CD, MLOps, and production-grade ML/AI system development.
Minimum Requirements
13+ years building production ML/AI systems including deployment, versioning, monitoring, optimization, with recent extensive generative AI and agentic system experience.
Proficient in ML frameworks (scikit-learn, PyTorch, TensorFlow, Keras), Python, PySpark, AWS services (SageMaker, Lambda), Docker, Kubernetes, and CI/CD tools.
Experience with LangGraph or comparable agentic AI frameworks, production LLM applications, RAG architectures, LLMOps practices, and vector databases like Pinecone.
Master's or PhD in Computer Science, Computer Applications or related field, or equivalent ML/AI engineering specialization/certifications.
Ideal Candidate Profile
Deep technical expertise in production-grade ML/AI engineering with a strong emphasis on generative AI and agentic AI system design and operations.
Experienced in end-to-end ML architecture and pipeline orchestration in an Agile/Scrum environment coordinating cross-functional teams.
Capable of leading complex technical integrations and providing informal technical leadership without formal management responsibilities.