ML Ops Engineer

Role: ML Ops Engineer supporting Lilly and NVIDIA's AI-driven drug discovery platform, focusing on building and maintaining scalable machine learning infrastructure. Responsibilities: manage the full ML lifecycle including deployment, monitoring, optimization, and model retraining; work with data scientists and engineers to ensure reliable AI model operation; automate platform processes using infrastructure-as-code and CI/CD practices; optimize GPU resources for research workloads. Requirements: Bachelor's in Computer Science, Engineering, or related field; 4+ years in ML engineering, MLOps, or platform engineering; strong Python skills; experience with ML frameworks (PyTorch, TensorFlow, JAX); proficiency in containerization (Docker, Kubernetes), cloud platforms (AWS, Azure, GCP), and automation tools (Terraform, GitHub Actions); familiarity with large-scale inference platforms (Triton, TensorRT). HighValue: AI/ML platform for biopharma research, GPU infrastructure, cloud/on-prem support, production ML deployment, collaboration with scientific teams. WorkSetup: hybrid (3 days onsite, 2 remote) at Silicon Valley hub.