Platform Engineer owning AWS, Kubernetes, observability, and Linux edge infrastructure. Improving reliability, automation, and developer workflows for Fleetworthy’s fleet - readiness technology.
About the role
- ML Platform Engineer at RAVL designing scalable machine learning platforms for financial services. Leading development on Azure Databricks and optimizing MLOps pipelines for enterprise environments.
Responsibilities
- Architect and lead development of ML platforms on Azure Databricks
- Design systems for training, feature engineering, model serving, and monitoring
- Build and standardize MLOps pipelines (CI/CD for ML, model versioning, deployment workflows)
- Extend Databricks with custom services, APIs, and integrations
- Integrate with enterprise systems (IAM, secrets, observability, governance)
- Optimize performance, scalability, and cost efficiency of ML workloads
- Define platform standards and engineering best practices
- Mentor engineers and guide technical direction
Requirements
- Deep experience building ML or data platforms at scale
- Strong expertise with Azure + Databricks (Spark, MLflow, jobs, clusters)
- Experience with MLOps tooling and model lifecycle management
- Strong backend engineering (Python/Scala/Java)
- Experience with distributed systems and data processing
- Familiarity with enterprise integrations (identity, security, observability)
Benefits
- 4 weeks paid time off (plus public holidays)
- Paid professional development days and continuous learning opportunities
- Comprehensive health & dental coverage, including mental health support
- Commitment to lifelong learning — continuous improvement through training, mentorship, and certification
- Variable bonus & RRSP contributions tied to performance and delivery impact
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