Danaher

Staff DevOps Automation Engineer - USA Remote

New York, NY, US$180,000-$220,000Posted 1 day ago

Job Description

Bring more to life.

At Danaher, our work saves lives. And each of us plays a part. Fueled by our culture of continuous improvement, we turn ideas into impact – innovating at the speed of life.

Our 60,000+ associates work across the globe at more than 15 unique businesses within life sciences, diagnostics, and biotechnology.

Are you ready to accelerate your potential and make a real difference? At Danaher, you can build an incredible career at a leading science and technology company, where we’re committed to hiring and developing from within. You’ll thrive in a culture of belonging where you and your unique viewpoint matter.

Learn about the Danaher Business System which makes everything possible.

The Staff DevOps Engineer owns the delivery and operational backbone of the platform behind our AI initiatives — CI/CD, infrastructure as code, environment management, observability, and production operations. You will work directly with the teams building AI into consequential work across the company: molecular design, autonomous labs, supply chain, professional services, and more. You will set the technical patterns others build on and help define what delivery infrastructure looks like as workloads become increasingly LLM-driven and agentic.

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

  • Design, build, automate, and operate secure, scalable cloud infrastructure across Azure using Infrastructure as Code (Terraform, Pulumi, etc.), while embedding governance, compliance, security, data quality, and auditability by design.
  • Develop and maintain the platform engineering ecosystem, including CI/CD pipelines, self-service capabilities, and resource provisioning frameworks that enable data scientists and engineers to rapidly and safely access compute, data, and ML resources

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