Senior Engineer - ML Operations

Danaher Corporation

9d•$180,000 - $220,000•Remote

About The Position

The Senior MLOps Engineer owns significant parts of the machine learning and agentic AI lifecycle supporting Danaher’s AI-driven products and automations. The role takes models and AI agents from experimentation into reliable, scalable production, and designs and operates pipelines, serving infrastructure, orchestration, and observability to support reproducible, rigorous AI development and operation. The engineer partners with teams across Danaher’s operating companies to turn AI prototypes into governed production-grade automations that run efficiently at scale on shared compute. The position is part of the Chief Information Officer (CIO) Office, is located in Washington, DC, and is fully remote. It reports to the Senior Director, Data and AI Platform. The role also provides technical leadership, establishes MLOps and LLMOps standards, and develops self-service tooling and reusable patterns that help teams build, evaluate, deploy, and monitor models and agents.

Requirements

Degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
At least 4 years of software, ML, or infrastructure engineering experience, including hands-on MLOps/LLMOps and a track record of taking ML models or AI agents into production at scale.
Strong experience with ML and LLM lifecycle tooling, including experiment tracking, evaluation, observability/monitoring, model registry, versioning, lineage, and reproducibility.
Strong experience with containerization and orchestration using Docker and Kubernetes, including scaling GPU and inference workloads.
Experience with a major cloud platform; Azure is preferred, including its AI services such as Azure ML or Azure AI Foundry.
Experience using infrastructure as code (IaC) and CI/CD for ML.
Proficiency in Python and familiarity with Bash for automation, tooling, and pipeline development.
Ability to travel up to 10%.

Nice To Haves

Experience building and operating production agentic systems, including multi-agent orchestration, tool calling, retrieval-augmented generation (RAG), and agent security practices such as sandboxing, permission setting, and prompt-injection defenses.
Experience operating ML or AI systems in a regulated environment such as GxP, SOX, or HIPAA, including model traceability, audit evidence, and AI governance controls.
Experience with high-performance LLM serving and AI gateway platforms such as vLLM, TGI, Triton, or LiteLLM, or serverless GPU compute platforms.

Responsibilities

Own the end-to-end ML and LLM lifecycle and deployment, including experiment tracking, model and prompt registries, versioning, lineage, and reproducibility.
Design and operate batch and low-latency online model-serving infrastructure, including LLM inference, autoscaling, GPU efficiency, and performance optimization.
Partner with engineering and business teams to productionize agentic workflows and automations as scalable, reliable services.
Use orchestration frameworks and provide containerized, sandboxed execution, secure tool and data integrations, and support for concurrent users and workflows.
Implement CI/CD, testing, offline and online evaluation, guardrails, and safe deployment patterns such as blue/green and canary deployments.
Monitor model and agent quality, drift, latency, token usage, and cost; implement tracing, automated alerting, and feedback loops.
Improve AI workload compute and cost efficiency through GPU and accelerated-compute scheduling, quota and utilization management, LLM provider and gateway routing, rate limits, and driver/CUDA image hygiene.
Partner with the platform team to maximize the value of contended, high-demand compute resources.
Develop golden paths and reusable self-service templates that help teams build, evaluate, deploy, and monitor models and agents.
Set MLOps and LLMOps standards and best practices while contributing hands-on to architecture and delivery.