Principal Platform Engineer, Machine Learning
Indexed description
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
- 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)
- Kubernetes and containerized ML workloads
- Feature stores and real-time inference systems
- Platform-first and systems-oriented
- Strong ownership and technical leadership
- Pragmatic with a focus on scalability
- 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
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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