ML Ops Engineer

Role: ML Ops Engineer supporting AI-driven drug discovery initiatives at Lilly in collaboration with NVIDIA, focusing on building and maintaining scalable machine learning platforms for research and development. Responsibilities: deploy, monitor, optimize, and automate ML models and inference workloads; ensure operational reliability; collaborate with scientists and engineers to integrate AI models into discovery workflows; improve model performance; implement infrastructure-as-code and CI/CD pipelines. Requirements: Bachelor’s in CS, Engineering, or related field; 4+ years in ML engineering or platform operations; strong Python skills; experience with ML frameworks (PyTorch, TensorFlow, JAX), MLOps tools (MLflow, Weights & Biases), containerization (Docker, Kubernetes), cloud (AWS, GCP, Azure), and large-scale inference technologies (Triton, TensorRT). Preferred: familiarity with distributed compute environments (Ray, Slurm), automation tools (Terraform, Ansible), observability practices, and supporting GPU infrastructure. HighValue: AI in drug discovery, production ML platform management, collaboration across research and engineering teams. Location & Work Setup: Silicon Valley hub with hybrid model (3 days onsite, 2 remote). This role demands technical excellence in MLOps, cloud infrastructure, and AI model deployment within a pharma R&D context.