[Hiring] Senior Engineer - ML Operations @Danaher Corporation

Role Description

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:

• 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)
  • Utilize 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
  • Partner 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
  • Set MLOps and LLMOps standards and best practices while staying hands-on with architecture and delivery

Qualifications

• Degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience
• 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: experiment tracking, evaluation, observability/monitoring, model registry, versioning, lineage, and reproducibility (e.g., MLflow, Weights & Biases, Langfuse)
• Strong experience with containerization and orchestration (Docker, Kubernetes), including scaling GPU and inference workloads, and with a major cloud platform (Azure preferred) and its AI services (e.g., Azure ML, Azure AI Foundry), using IaC and CI/CD for ML
• Proficiency in Python (and familiarity with Bash) for automation, tooling, and pipeline development

Requirements

• Ability to travel – up to 10%

Benefits

• Comprehensive, competitive benefit programs including paid time off, medical/dental/vision insurance, and 401(k) to eligible employees
• Remote work arrangement eligible for this position
• Annual salary range for this role is $180K-$220K
• Eligible for bonus/incentive pay