MLOps Engineer (Scientific Platforms / Data Foundry)

Quick Facts

• Role: MLOps Engineer (Scientific Platforms / Data Foundry)

Description

Build ML deployment pipelines, model serving infrastructure, APIs, and observability guardrails that make Data Foundry’s scientific discovery tools reliable and scalable for both human users and autonomous AI agents. Operationalize predictive and analytical methods into analytics-ready, well-monitored prototypes with response-time and error-handling guarantees. Partner across analytical methods and agile scientific data infrastructure to expose tools through well-defined interfaces.

Responsibilities

• Build and maintain end-to-end ML deployment pipelines (experiment tracking, model versioning, containerized serving, automated retraining triggers)

• Develop model registry infrastructure and feature engineering pipelines

• Implement monitoring/alerting for data pipelines, APIs, ML models, and agentic workflows (LLMOps)

• Create dashboards and metrics for pipeline execution, API latency, token usage, prediction quality, and system health

• Establish structured logging and tracing for debugging and performance optimization

• Deploy analytical/predictive scientific methods with versioning, structured error handling, and response-time guarantees

• Support both synchronous and asynchronous workloads (interactive queries and batch/agent-invoked jobs)

• Define API contracts, documentation standards, and testing frameworks for external teams

• Build and operate cloud-native model serving infrastructure using containers and Kubernetes with infrastructure-as-code

• Implement CI/CD for ML models (validation, A/B testing, canary deployments, rollback)

• Integrate model serving with versioned training/inference data pipelines

• Collaborate with Frontier AI/Tech partners to expose tools via REST APIs and MCP-compatible endpoints

• Ensure deployed models include uncertainty quantification and confidence metrics

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

• Authorized to work in the United States on a full-time basis; no visa sponsorship

Benefits

• Eligibility for a company bonus (performance-based)

• Comprehensive benefits: medical, dental, vision, prescription drug benefits, flexible benefits, life insurance and death benefits

• 401(k) with company sponsorship; pension

• Vacation and well-being benefits, including employee assistance, fitness benefits, and employee clubs/activities