About this role
Job title: ML Platform Engineer - Databricks & AWS SageMaker
About the Role You will join a team responsible for building and scaling the ML platform used by 300+ data scientists and ML engineers across 60+ teams. Your work will directly impact how machine learning models are deployed to production — reliably, securely, and at scale. You’ll spend most of your time implementing and standardising Databricks MLOps (environments, CI/CD pipelines, model monitoring) while also contributing to the evolution of a battle‑tested AWS SageMaker platform.
What You'll Do
- Design, build, and scale a production-grade ML / MLOps platform
- Implement Databricks MLOps (environments, CI/CD pipelines, monitoring)
- Maintain and improve the AWS SageMaker ML platform
- Automate infrastructure and workflows using Python and Infrastructure‑as‑Code (Terraform)
- Design solutions for a large-scale multi-tenant architecture (60+ teams, 3 AWS accounts)
- Partner closely with data scientists and ML engineers to enable production model deployments
- Ensure platform reliability, security, observability, and cost efficiency
What We're Looking For
- 4–6 years of experience building production infrastructure for ML or data workloads
- Deep knowledge of Databricks or AWS SageMaker (Databricks strongly preferred)
- Strong in AWS fundamentals (IAM, networking, compute)
- Proficient in Python automation and Terraform / IaC
- Understanding of the full ML lifecycle: training, versioning, deployment, monitoring
Nice to Have
- Databricks experience strongly preferred
Compensation & Benefits
- Salary not disclosed; details not provided