ML Ops Engineer

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 work—but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us.

Where AI Meets Medicine:
Build the Future of Drug Discovery in the Heart of Silicon Valley! Making medicine that’s never been made means doing what’s never been done. If you’re an engineer, scientist, or builder who thrives on problems no one has solved before, this is your invitation; we want you on the team. We are ready to challenge the status quo and push medicine forward, all in the name of health.

Are you up for the challenge? If so, join us!

Lilly and NVIDIA are launching a new AI co‑innovation lab in the heart of Silicon Valley — an up-to-$1 billion, multi-year commitment to solve drug discovery’s toughest challenges. The lab brings Lilly scientists, technologists, chemists and biologists together with NVIDIA engineers under one roof. Together, we are building purpose‑built foundation and frontier AI models trained on Lilly data at scale, tightening the feedback loop between automated wet labs and computational dry labs, designing the next generation of medicines for millions of patients across the globe.

As an ML Ops Engineer, you build and operate the platforms that run the end-to-end machine learning lifecycle. You enable reliable model deployment, operation, monitoring, retraining, and reproducibility  optimize infrastructure and GPU resources to support research and discovery workloads. You will work closely with engineering and scientific teams to deliver production-ready AI capabilities.

Lead the operational lifecycle of ML models, including deployment, monitoring, and ongoing reliability. Operate and optimize large-scale inference platforms that support scientific discovery and AI workloads. Ensure models can be deployed, scaled, monitored, and maintained in production environments. Test, refine, and improve model accuracy. Work with data scientists, business analysts and partners to integrate ML models into broader strategies. Automate the platform with infrastructure-as-code and CI/CD, and document it well enough that someone else can operate it.

• Strong Python skills and experience working with machine learning frameworks such as PyTorch, JAX, or Tensor Flow.
• Experience deploying, operating, and scaling production machine learning platforms, including model serving, monitoring, and large-scale inference workloads.
• Experience with MLOps platforms and tools like MLflow, Weights & Biases, KServe, or similar technologies.
• Proficiency with containerization, orchestration, and distributed compute environments (Docker, Kubernetes, Slurm, Ray).
• Experience operating large-scale AI platforms that deploy, host, and optimize machine learning models for production use, using technologies such as Triton, vLLM, or TensorRT-LLM.
• Experience with infrastructure automation and CI/CD practices using tools such as Terraform, Ansible, Git Hub Actions, or related.
• Experience supporting cloud platforms (AWS, Azure, or GCP) and on-premises GPU infrastructure.
• Knowledge of observability and operational monitoring, including metrics, logging, tracing, and performance…