Senior Machine Learning Engineer (Cloud & Data Platform) - |5-8+ years of Exp.| Canada
TMS LLC
About the role
Company Description
Job Description
Role: Senior Machine Learning Engineer (Cloud & Data Platform)
Duration: Long Term
Location: Montreal, Canada – Hybrid
We are seeking a highly capable Senior Machine Learning Engineer to support the modernization of enterprise analytics and modeling platforms. This role focuses on migrating and transforming legacy data and machine learning workflows into scalable, cloud‑native architectures while improving performance, reliability, and engineering standards.
The ideal candidate combines strong ML engineering expertise with deep experience in distributed data processing and cloud data platforms.
Responsibilities
🔹 Machine Learning Engineering
- Design, develop, and deploy scalable machine learning models using modern frameworks (e.g., PyTorch)
- Re‑engineer and optimize legacy models into efficient, production‑grade implementations
- Improve model performance, scalability, and reproducibility
- Support model validation, benchmarking, and certification processes
- Ensure full traceability and documentation of model logic and outputs
🔹 Data Platform & Pipeline Engineering
- Design and optimize distributed data pipelines using Spark‑based platforms (e.g., Databricks)
- Build and refactor ETL/ELT workflows for performance and scalability
- Implement data models within modern cloud data warehouses (e.g., Snowflake)
- Apply best practices for cloud‑native data architecture
- Standardize reusable utilities and frameworks for analytics workflows
🔹 Cloud Migration & Modernization
- Participate in migration of on‑prem or legacy analytics platforms to cloud ecosystems
- Refactor existing codebases to align with modern engineering and DevOps standards
- Leverage cloud compute capabilities (including GPU acceleration where applicable)
- Support scheduling and orchestration of data and ML workflows
🔹 Testing, Validation & Governance
- Conduct rigorous testing and validation to ensure data and model accuracy
- Perform parallel runs and benchmarking when modernizing systems
- Collaborate with governance, risk, and compliance stakeholders
- Maintain high standards of documentation and reproducibility
Additional Information
All your information will be kept confidential according to EEO guidelines.
Responsibilities
- Design, develop, and deploy scalable machine learning models using modern frameworks (e.g., PyTorch)
- Re-engineer and optimize legacy models into efficient, production-grade implementations
- Improve model performance, scalability, and reproducibility
- Support model validation, benchmarking, and certification processes
- Ensure full traceability and documentation of model logic and outputs
- Design and optimize distributed data pipelines using Spark-based platforms (e.g., Databricks)
- Build and refactor ETL/ELT workflows for performance and scalability
- Implement data models within modern cloud data warehouses (e.g., Snowflake)
- Apply best practices for cloud-native data architecture
- Standardize reusable utilities and frameworks for analytics workflows
- Participate in migration of on-prem or legacy analytics platforms to cloud ecosystems
- Refactor existing codebases to align with modern engineering and DevOps standards
- Leverage cloud compute capabilities (including GPU acceleration where applicable)
- Support scheduling and orchestration of data and ML workflows
- Conduct rigorous testing and validation to ensure data and model accuracy
- Perform parallel runs and benchmarking when modernizing systems
- Collaborate with governance, risk, and compliance stakeholders
- Maintain high standards of documentation and reproducibility
Skills
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