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.