Engineer – MLOps, Scientific Platforms

Role Overview

• 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
• build and maintain end-to-end ML deployment pipelines: experiment tracking, model versioning (MLflow, Weights & Biases)
• develop model registry infrastructure and feature engineering pipelines that enable computational scientists to access models
• implement monitoring and alerting for data pipelines, APIs, ML models, and agentic systems (LLMOps)
• build dashboards and metrics tracking for pipeline execution, API latency, token usage, model prediction quality, and system health
• establish structured logging and tracing infrastructure for debugging and performance optimization across scientific data systems
• deploy predictive and analytical methods with versioning, structured error handling, and response-time guarantees
• productionize when and where needed in partnerships with Tech@Lilly
• build serving infrastructure supporting both synchronous and asynchronous workloads
• define and implement API contracts, documentation standards, and testing frameworks that ensure scientific tools are analysis ready

Requirements

• 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, TensorFlow, 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, LangChain) 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) .

Tech Stack

• AWS
• Azure
• Cloud
• Google Cloud Platform
• GRPC
• Kubernetes
• Python
• PyTorch
• Scikit-Learn
• Tensorflow

Benefits

• eligibility for medical, dental, vision and prescription drug benefits
• flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts)
• life insurance and death benefits
• certain time off and leave of absence benefits
• well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities)
• company bonus (depending, in part, on company and individual performance)