Engineer – MLOps, Scientific Platforms

Responsibilities

• Operationalize Data Foundry’s scientific tools and analytical methods into actionable prototypes
• Build the ML deployment pipelines, model serving infrastructure, API layers, and observability guardrails
• Ensure every scientific tool Data Foundry produces are analytics‑ready, well‑monitored, and exposed through APIs
• Maintain end‑to‑end ML deployment pipelines: experiment tracking, model versioning, containerized model serving
• Develop model registry infrastructure and feature engineering pipelines
• Implement monitoring and alerting for data pipelines, APIs, ML models, and agentic systems to ensure system reliability and performance at scale
• Productionize predictive and analytical methods from Methods4
  
  Insight with versioning and structured error handling
• Build serving infrastructure supporting both synchronous and asynchronous workloads
• Define and implement API contracts, documentation standards, and testing frameworks
• Build and operate cloud‑native model serving infrastructure using containers, Kubernetes, and infrastructure‑as‑code
• Develop CI/CD pipelines for ML models and integrate model serving with Data Foundry’s data pipelines
• Collaborate with the Frontier AI team and Tech@Lilly to ensure Data Foundry’s scientific tools are exposed via well‑defined interfaces

• B.S. or M.S. in Computer Science, Data Science, Machine Learning, Bioinformatics, Computational Biology, or related field
• 3+ years of experience in MLOps, ML engineering, or scientific platform development
• Qualified applicants must be authorized to work in the United States on a full‑time basis
• Strong Python skills; experience with ML frameworks (PyTorch, Tensor Flow, scikit‑learn) and ML lifecycle tools (MLflow, W&B, Kubeflow, or similar)
• Proven track record building and deploying production model serving infrastructure—containerized endpoints, RESTful/gRPC APIs, and operational monitoring
• Working knowledge of cloud platforms (AWS, Azure, or GCP), Kubernetes, and CI/CD automation
• Strong communication skills with ability to collaborate across computational scientists, software engineers, and partner teams
• Experience operationalizing scientific or computational models (cheminformatics, bioinformatics, structural biology, QSAR, molecular simulations, PK/PD, systems biology, or ODE‑based models)
• Hands‑on experience with model monitoring, drift detection, and automated retraining systems
• Familiarity with API gateway patterns, event‑driven architectures, and service mesh technologies
• Experience with feature stores, data versioning (DVC), or experiment tracking at scale
• Exposure to AI agent frameworks (MCP, Lang Chain) or building APIs that AI systems invoke programmatically
• Experience with C, C++, CUDA, or GPU‑accelerated computing for optimizing model training/inference performance; familiarity with containerizing HPC workloads (Singularity/Apptainer)