Quick overview
Job description
RAVL is a boutique technology advisory and engineering firm focused on the financial services industry. Everything we do is centered on helping our clients realize measurable ROI from their technology investments.
We’re growing our engineering team and hiring ML Platform Engineers to design and build scalable machine learning platforms across clients. This includes developing cloud-native ML infrastructure, enabling MLOps capabilities, and supporting end-to-end model lifecycle management in enterprise environments.
These roles include both immediate project needs and pipeline hiring for upcoming engagements, with a focus on building reliable, production-grade ML systems at scale.
What does success look like in this role?
- 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
Great, do my skills fit?
- 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)
Nice to have skills
- Kubernetes and containerized ML workloads
- Feature stores and real-time inference systems
Mindset Traits
- Platform-first and systems-oriented
- Strong ownership and technical leadership
- Pragmatic with a focus on scalability
Why work at RAVL?
- Flexible, client-aligned work model — autonomy with accountability, adapting to client delivery needs
- Variable bonus & RRSP contributions tied to performance and delivery impact.
- 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
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