Senior Machine Learning Engineer

Job Title: Senior Machine Learning Engineer

Location: Surrey, BC (Hybrid)
    
Estimated Duration: 6 months

Job Description: 

Our client, a Software Developer in the Medical Industry, is looking for a Senior Machine Learning Engineer for their Surrey BC location
 
 
Key Responsibilities
 

• Architect, build, and maintain scalable and reliable data pipelines (ETL/ELT) to process and transform large-scale datasets for model training and analysis.
• Lead the design and implementation of production-grade MLOps systems, including CI/CD/CT pipelines for automated data validation, model training, and deployment.
• Develop and manage data architectures, including databases, data warehouses, and data lakes, to ensure data quality, integrity, and accessibility.
• Optimize ML model inference for low latency and high throughput to meet the demands of real-time clinical use.
• Implement comprehensive monitoring for both data pipelines (data quality, freshness, lineage) and ML models (performance, concept/data drift).
• Manage the end-to-end data and ML infrastructure on cloud platforms using containerization (Docker) and orchestration (Kubernetes).
• Collaborate with data scientists to productionalize prototype models and with software engineers to integrate them into user-facing applications.
• Establish and enforce company-wide best practices and documentation for both data engineering and MLOps.
• Mentor other engineers and data scientists on building efficient data solutions and robust ML systems.

 
 
Required Skills and Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related quantitative field.
• 7+ years of professional experience in data engineering and/or machine learning engineering, with at least 4+ years of hands-on experience building production data pipelines AND deploying ML models.
• Expert-level proficiency in Python and a strong command of SQL.
• Deep hands-on experience with big data processing frameworks like Apache Spark, Hadoop, or Dask.
• Strong experience with ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
• Proven experience with MLOps tools and practices (e.g., MLflow, Kubeflow, Kubeflow Pipelines).
• Expertise in cloud platforms (AWS, GCP, or Azure) and proficiency with containerization and orchestration technologies (Docker, Kubernetes).
• In-depth knowledge of various database technologies, including SQL (e.g., PostgreSQL) and NoSQL (e.g., MongoDB).
• Excellent problem-solving skills with a demonstrated ability to architect complex, end-to-end data and ML systems.
• Strong leadership, mentorship, and communication skills.

 
 
Preferred Qualifications:

• Experience in the healthcare, life sciences, or medical imaging domain.
• Knowledge of real-time data streaming technologies (Kafka, Apache Flink, or Kinesis).
• Familiarity with Infrastructure as Code (IaC) tools like Terraform or CloudFormation.
• Knowledge of data governance and data privacy standards (e.g., HIPAA, GDPR).