Senior Engineer - ML Operations

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The Senior MLOps Engineer is responsible for owning significant parts of the machine learning and agentic AI lifecycle that powers Danaher’s next generation of AI-driven products and automations. You will take models and AI agents from experimentation to reliable, scalable production. In this high-impact role, you will design and operate the pipelines, serving infrastructure, orchestration, and observability that let data scientists, engineers, and business teams build and run ML models and agentic workflows with speed, reproducibility, and rigor. You will partner with teams across Danaher’s operating companies to turn promising AI prototypes into governed, production-grade agentic automations that run efficiently at scale on shared compute.

This position reports to the Senior Director, Data and AI Platform and is part of the Chief Information Officer (CIO) Office located in Washington, DC and will be fully remote.

In this role, you will have the opportunity to

Own the end-to-end ML and LLM lifecycle and deployment: experiment tracking, model and prompt registries, versioning, lineage, and reproducibility (e.g., MLflow, Weights & Biases); design and operate model serving for batch and low-latency online inference, including LLM inference, with autoscaling, GPU efficiency, and performance optimization (batching, quantization, caching).

Partner with engineering and business teams to productionize agentic workflows and automations: turn agent prototypes into scalable, repeatable, reliable services using orchestration frameworks (e.g., LangGraph, Claude Agent SDK, Airflow, Prefect, etc) with containerized, sandboxed execution, secure tool and data integrations (e.g., MCP), and support for many concurrent users and workflows without contention.

Implement CI/CD, evaluation, and observability for ML and agents: automate the path from code to validated production through testing, offline and online evaluations, guardrails, and safe deployment patterns (blue/green, canary); monitor model and agent quality, drift, latency, token usage, and cost; implement tracing, automated alerting, and feedback loops using observability tools (e.g., OpenTelemetry).

Drive compute and cost efficiency for AI workloads: GPU and accelerated-compute scheduling, quota and utilization management, LLM provider/gateway routing and rate limits, and driver/CUDA image hygiene, partnering with the platform team to maximize value from contended, high-demand resources.

Build self-service ML and agent tooling and provide technical leadership: develop golden paths and reusable templates that let teams build, evaluate, deploy, and monitor models and agents without deep infrastructure expertise, treating AI enablement as a product; set MLOps and LLMOps standards and best practices while staying hands-on with architecture and delivery.