Software Engineer, AI Engineer (Applied​/Software), Machine Learning​/ ML Engineer

Work Your Magic with us! Start your next chapter and join Millipore Sigma.

Ready to explore, break barriers, and discover more? We know you’ve got big plans – so do we! Our colleagues across the globe love innovating with science and technology to enrich people’s lives with our solutions in Healthcare, Life Science, and Electronics. Together, we dream big and are passionate about caring for our rich mix of people, customers, patients, and planet.

That's why we are always looking for curious minds that see themselves imagining the unimaginable with us.

This role does not offer sponsorship for work authorization. External applicants must be eligible to work in the US.

We are looking for a Staff Applied AI Engineer to build scalable, production-grade backend applications that leverage large and complex datasets. This role is software engineering first, with a strong emphasis on designing reliable systems that ingest, process, and serve data to power modern applications and AI-driven solutions.

You will work closely with product managers, software engineers, data scientists, and ML engineers to build robust backend services, data-intensive applications, and production AI systems. Success in this role requires strong software engineering fundamentals, practical experience working with data throughout its lifecycle, and the ability to design systems that are scalable, maintainable, and reliable in production.

• Lead technical strategy for AI/LLM systems across multiple products
• Architect retrieval, orchestration, agentic, and evaluation systems that run reliably in production
• Set the standards for AI safety, evaluation, observability, and responsible rollout in aregulatedcontext
• Mentor Junior-level engineers into strong AI engineers;
  Employ AI Native development skills to multiply the productivity (Claude, etc.)
• Lead the frontier: evaluate new models, techniques, and tools, and bring the right ones into the team
• Design, develop, and maintain scalable Python applications and backend services.
• Build systems that ingest, validate, transform, and manage structured and unstructured data in production environments.
• Design data models and storage solutions that support scalable, high-performance applications.
• Develop reusable components for data processing, validation, enrichment, and feature generation.

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related quantitative field.
• At least 3 years of hands-on experience in machine learning, data science, search relevance, or ranking systems.

• At least 5 years of software engineering experience, with deep recent time leading production AI/LLM systems
• Proven expertise in Python and ML frameworks (MLFlow, Tensor Flow, PyTorch, Scikit- learn, or equivalent).
• Strong background in statistical analysis, data exploration, and working with large-scale datasets.
• Experience with feature engineering, data preprocessing, and data
• Seasoned hands‑on coder; still writes production Python regularly
• Seasoned system designer for AI systems at scale, retrieval, agents, evaluation, latency, and cost, vector databases/pipelines
• Strong experience building and maintaining production‑grade backend applications.
• Experience designing and developing RESTful APIs and distributed systems.
• Strong SQL skills and experience working with relational databases; familiarity with No
  
  SQL databases or modern data storage technologies is a plus.
• Solid understanding of data engineering fundamentals, including data quality, validation, transformation, modeling, and efficient storage.
• Experience designing systems that process large datasets reliably and efficiently.
• Experience with cloud platforms such as Google Cloud Platform (GCP) or AWS.
• Experience using Docker, Git, CI/CD pipelines, automated testing frameworks, and modern software engineering best practices.

• Languages:
  
  Python, REST API, Pandas, Num Py
• Cloud and infrastructure: AWS Services and/or GCP, Kubernetes, Bedrock
• Distributed systems: event-driven architectures, including Kafka
• Orchestration Frameworks:
  Lang Graph, Lang Chain, Air Flow,…