LEAD MACHINE LEARNING OPS ENGINEER

About the role

At SAS, machine learning has moved well beyond experimentation. As part of our ongoing Digital & IT transformation, we are expanding our investments in machine learning, automation, data platforms, MLOps, and Generative AI. We design, deploy, and operate production models that steer pricing decisions, personalize customer experiences, and make our operations smarter every day. Delivering business value from machine learning requires more than great models. It requires robust platforms, strong engineering practices, and operational excellence. As our Lead MLOps Engineer, you will be a thought leader who sets the standard for how machine learning is engineered and operated across SAS. You will shape the future of our ML platform, drive technical excellence, influence architecture and standards, and ensure our production ML ecosystem remains scalable, reliable, observable, and secure.

If you're ready to lead the next chapter of machine learning engineering at SAS, this is the role for you!

We are looking for a Lead MLOps Engineer to own the end-to-end ML lifecycle at SAS, from the way models are built and validated, through deployment and serving, to how they are monitored, maintained, and retrained in production. This is a thought leadership role as much as a hands-on engineering role.

You will help shape how AI is engineered and operated at scale within SAS, establishing the foundations that enable machine learning, Generative AI, and future AI capabilities to move efficiently from experimentation to production.

You will define what "production-ready" means for ML at SAS and make it a team-wide standard. You will work closely with data scientists to engineer models for operability from the start, collaborate with developers and operations roles to improve how model ops are structured and handed over, and partner with Data Engineering and IT to mature the underlying platform. You report to the Head of AI & Automation.

Key Responsibilities

• Define and own the MLOps vision and standards for the AI & Automation team, covering the full lifecycle from experiment to production to retraining.

• Establish what "production-ready" looks like for ML at SAS: packaging, testing, documentation, monitoring hooks, and rollback procedures built in from the start.

• Work directly with data scientists during model development to ensure models are designed for operability.

• Design and improve the handover process between the in-house ML team and the offshore operations team, creating clarity, structure, and shared standards.

• Build and mature the MLOps platform on Azure: model registry, CI/CD pipelines for ML, automated retraining, feature management, and deployment infrastructure.

• Establish model monitoring and observability frameworks, defining what to track, how to alert, and how to act when model performance degrades.

• Drive adoption of MLOps best practices across the team through documentation, templates, review processes, and active coaching.

• Evaluate and introduce MLOps tooling and frameworks (MLflow, Azure ML, etc.) where they improve the team's ability to operate at scale.

• Collaborate with Data Engineering and IT on infrastructure, security standards, and cost-efficient operation of the ML platform.

• Contribute to the broader AI & Automation technical roadmap alongside the Head of AI & Automation and the Data & AI Architecture team.

The Team

We are a central AI & Automation function within SAS Digital & IT that develops and operates solutions across ML, MLOps, automation, and Generative AI. Our team of data scientists, AI engineers and ML engineers work together to deliver AI that creates real, measurable value.

As SAS continues to strengthen its AI capabilities, we are investing in modern cloud platforms, scalable AI solutions, engineering excellence, and AI-native ways of working that enable machine learning to create value across the business.

Today, model operations are largely handled by an offshore team. The ML team wants to evolve beyond that split, building a coherent end-to-end approach where production-readiness is built in from the start. This role exists to lead that shift.

You will be the senior MLOps voice in the team, setting the direction, establishing the standards, and working hands-on alongside data scientists and engineers to close the gap between model development and reliable production operations.

To be successful we believe you have

• Master's Degree in Computer Science, Engineering, Machine Learning, Mathematics, or a related field.

• At least 5 years of hands-on experience in ML engineering or MLOps, with a track record of owning production ML systems end-to-end.

• Deep expertise across the ML lifecycle: experiment tracking, model packaging, CI/CD for ML, deployment and serving, monitoring, drift detection, and automated retraining.

• Proven experience operating ML models in production on Azure (Azure ML, Azure Databricks, Azure Data Factory, or equivalent cloud platforms).

• Strong proficiency in Python; experience with MLflow or similar tools.

• Experience with containerisation and orchestration (Docker, Kubernetes) in a cloud environment.

• Demonstrated ability to set technical standards and influence engineering practices across a team, without necessarily having formal line management responsibility.

• Experience working with or alongside offshore or distributed engineering teams, including designing effective handover and collaboration processes.

• Experience with Infrastructure as Code (e.g. Terraform) on Azure.

• Familiarity with AI-native SDLC practices and Coding Assistants is a plus.

• Strong communication skills, able to translate MLOps complexity into clear direction for data scientists and into plain language for non-technical stakeholders.

• Experience with LLM-based systems, agentic AI, or GenAI engineering patterns is a plus

We believe you are a pragmatic technical leader who combines deep MLOps expertise with a genuine interest in raising the capability of the team around you. You are hands-on enough to earn credibility, structured enough to define standards that stick, and collaborative enough to bring both the team along with you. You see production reliability not as a constraint on speed, but as what makes speed sustainable.

Why SAS?

Join SAS at an exciting time of technological transformation. Digital & IT is modernizing its technology landscape, strengthening cloud-native capabilities, and expanding the use of AI across the company. As Lead MLOps Engineer, you will play a key role in building the engineering foundations that enable AI solutions to scale across SAS, influencing platform strategy, engineering standards, and operational excellence.

At SAS, we offer extensive opportunities for professional development in an international, fast-paced working environment. We are dedicated to the continuous growth of our employees. Working with us comes with a variety of benefits, including:

• Travel Perks: Enjoy discounted travel opportunities around the world with SAS.

• Health & Wellness: Access to health and wellness benefits, including a newly renovated gym with complimentary classes such as CrossFit and yoga.

• Discounts: Receive discounts from a wide range of brands, as well as on transportation to and from airports, airport shops, hotels, and car rentals.

• Work Environment: Our office location in Frösundavik offers a vibrant workspace with a restaurant, café, and easy access to outdoor activities in Hagaparken and Brunnsviken. Engage in running, tennis, outdoor gym sessions, kayaking, and stand-up paddling with equipment available free of charge.

• Convenient Commute: Benefit from a non-stop bus service connecting our office to Solna station, and commuter trains, alongside a network of cycle paths.

Our Culture at SAS

At SAS, we are dedicated to caring for each other, delighting our travelers, and driving the transformation towards sustainable aviation. As a future colleague on our team, you'll join a culture where we work collaboratively towards common goals, recognize each other's contributions, and celebrate successes. Our focus is on safe, sustainable, and punctual execution, and we are committed to protecting our planet while transforming SAS for the future. This is an empowering workplace where you can thrive, grow, and take ownership of your work. Join us at SAS and be part of shaping the future of aviation!

Additional information

• Apply by September 4, 2026. We review applications continuously and encourage you to apply early, as the position may be filled before the closing date. Please note that, due to GDPR regulations, we cannot accept applications via email.

• This is a full-time position (100%) based at our headquarters in Frösundavik, Solna, Stockholm. We value in-person collaboration, although flexible working arrangements may be available depending on team and business needs.

• As this role contributes to security-classified systems and business-critical capabilities, a background check will be conducted as part of the final stage of the recruitment process.

If you would like to learn more about the role, the team, or how AI, MLOps, and Generative AI are evolving at SAS, feel free to reach out to Warren Edgren, Head of AI & Automation, at warren.edgren@sas.se.

Please note that applications must be submitted through our careers site, as we are unable to process applications received by email.