AI Infrastructure Engineer

JOB DESCRIPTION

Work Schedule

Environmental Conditions

Job Description

As part of the Thermo Fisher Scientific team, you’ll discover meaningful work that makes a positive impact on a global scale. Join our colleagues in bringing our Mission to life every single day to enable our customers to make the world healthier, cleaner and safer. We provide our global teams with the resources needed to achieve individual career goals while helping to take science a step beyond by developing solutions for some of the world’s toughest challenges, like protecting the environment, making sure our food is safe or helping find cures for cancer.

The Position

This position is part of the Artificial Intelligence team within R&D, with colleagues across the USA, the Netherlands, the Czech Republic and France. Our team enables the development and adoption of AI across the organization by providing shared capabilities, tools and best practices that help R&D teams bring novel AI into our electron microscopy products.

As an ML-Ops / AI Infrastructure Software Engineer, your focus will be on accelerating AI research and development. You will work closely with data scientists and AI application developers to improve the way we develop, experiment with and integrate AI solutions.

Your work creates an effective AI development ecosystem that makes data and tools easier to use, improves reproducibility, reduces friction in development workflows and enables teams to move from ideas to working AI applications faster. In doing so, you will also contribute to increasing the overall AI engineering maturity of the organization.

How you will make an impact

You will help our AI teams work more efficiently by improving the infrastructure, tooling and development practices around machine learning and generative AI.

In this role, you will:

• Develop and improve shared ML-Ops and LLM-Ops tooling, workflows and infrastructure for AI research and application development.
• Optimize data and model development workflows while improving engineering practices around Git, testing, CI/CD, reproducibility and model lifecycle management.
• Maintain a reliable shared compute ecosystem across cloud and on-premise infrastructure, including tool integration, monitoring, capacity, uptime and disaster recovery.
• Enable data scientists and AI application developers to move faster through reusable components, automation and best practices.
• Work with R&D teams to identify bottlenecks, establish standards and improve AI maturity, with a strong focus on quality, automation and smooth handover to downstream software teams.

The Candidate

You enjoy working at the intersection of software engineering, machine learning and developer productivity, and are motivated by exploring and applying emerging technologies to accelerate innovation. You collaborate well across disciplines and enjoy creating solutions that help others work more effectively.

You bring:

• A BSc or MSc degree in software engineering, computer science or a related technical field, or equivalent relevant experience.
• Relevant hands-on software development experience, with strong practical skills in Python and infrastructure technologies such as Docker, Kubernetes and AWS.
• Experience with ML-Ops concepts and best practices, including experiment tracking, model and artifact management, evaluation, reproducibility and lifecycle management.
• Experience with data-intensive workflows, including object storage such as S3, data traceability, lineage and reliable movement of data across development workflows.
• Experience with, or strong familiarity with, LLM-Ops, including evaluation, observability, reproducibility and quality assurance for agentic and generative AI systems.
• A quality-oriented mindset and a strong understanding of software engineering practices, testing, automation and CI/CD.
• Strong communication and collaboration skills, with the ability to work effectively with data scientists, AI developers and software engineers.
• A proactive mindset and an interest in helping establish standards, share knowledge and improve ways of working across teams.