MLOps Engineer

The Role:

• We're looking for an MLOps Engineer to build and operate the platform that gets our machine learning and AI work into production reliably.
• You'll own the lifecycle tooling and infrastructure that lets data science and engineering teams train, track, deploy, and monitor models without reinventing the wheel each time.
• This is a hands-on, senior-individual-contributor role: you'll set technical direction in your area and mentor less-experienced engineers, while spending most of your time building.

What You Will Do:

• Build and operate the ML lifecycle platform, with MLflow at the center: experiment tracking, model registry, artifact and metadata management, and versioned, repeatable training and inference pipelines.
• Own CI/CD and deployment for ML workloads, including packaging, containerization, automated testing and validation, staged rollouts, and rollback.
• Make models observable and reliable in production, standing up monitoring for model and service health with latency, drift, data-quality, and cost signals.

You Have:

• 4–7 years of relevant experience in platform engineering, DevOps, or MLOps, with solid experience operating systems in production.
• Hands-on experience with ML lifecycle tooling using MLflow or similar platforms.
• Strength in cloud-native infrastructure with Kubernetes, containerized workloads, and infrastructure-as-code tools such as Terraform.