Senior Machine Learning Engineer

Contract Duration: 6 months to start, high likelihood of extension.

Location: Open to candidates Canada Wide, PST

Job Summary:

On behalf of our client, Affinity is seeking a highly experienced Senior Machine Learning Engineer to support the deployment, operationalization, monitoring, and ongoing management of enterprise AI and machine learning solutions.

This is a hands-on technical role focused primarily on MLOps, production deployment, CI/CD automation, model lifecycle management, and cloud-based machine learning infrastructure. The successful candidate will work closely with Data Scientists to take machine learning solutions from development through production and ensure they remain secure, scalable, reliable, and well-governed.

The ideal candidate brings deep experience deploying and supporting machine learning solutions in cloud environments and has successfully implemented automated ML pipelines, model monitoring, endpoint management, and production support processes. While experience with Generative AI, RAG architectures, and modern AI frameworks is considered an asset, the primary focus of this role is operationalizing and maintaining AI/ML solutions in production environments.

Key Responsibilities

MLOps & Production Operations

- Design, implement, and maintain enterprise-grade MLOps frameworks and deployment pipelines
- Build and support CI/CD processes for machine learning solutions
- Manage model deployment workflows, endpoint configurations, and release processes
- Implement model monitoring, alerting, drift detection, and operational health checks
- Ensure machine learning solutions meet reliability, scalability, security, and governance requirements
- Support production incident management, troubleshooting,



and root cause analysis

Machine Learning Platform Engineering

- Develop and maintain scalable ML infrastructure within cloud environments
- Automate model training, validation, deployment, and retraining workflows
- Manage model registries, version control processes, and lifecycle management practices
- Implement containerized deployment strategies using Kubernetes and related technologies
- Collaborate with architecture, cloud, and platform teams to optimize AI infrastructure

Collaboration with Data Science Teams

- Work closely with Data Scientists to productionize machine learning models
- Support feature deployment, model integration, and production readiness reviews
- Assist with feature engineering pipelines and model packaging when required
- Provide technical guidance on deployment strategies, monitoring approaches, and operational best practices

Governance & Responsible AI

- Implement monitoring and controls to support model governance requirements
- Support security, compliance, fairness, auditability, and operational risk management initiatives
- Participate in AI lifecycle management and model review processes
- Contribute to standards and best practices within the AI Centre of Excellence

Required Experience

- 7+ years of experience in Machine Learning Engineering, MLOps Engineering, AI Platform Engineering, DevOps Engineering, Data Engineering,



or a related discipline
- Proven experience deploying and supporting machine learning solutions in production environments
- Experience designing and implementing CI/CD pipelines for ML workloads
- Robust experience with cloud-native machine learning platforms
- Experience with MLflow, model registries, deployment pipelines, and automated ML lifecycle processes
- Strong understanding of model monitoring, model performance management, and production operations
- Hands-on experience with containerization and orchestration technologies such as Docker and Kubernetes
- Experience supporting enterprise-scale machine learning platforms and applications

Preferred Experience

- Azure Machine Learning
- Azure AI Foundry
- Databricks
- MLflow
- GitHub Actions
- Azure DevOps
- RAG (Retrieval-Augmented Generation) architectures
- Generative AI application deployment
- Semantic Kernel
- LangGraph
- LLM evaluation and monitoring frameworks

Top Skills Necessary

- 7+ years of experience deploying and operationalizing Machine Learning models in production environments working across the full machine learning lifecycle, including deployment, maintenance, monitoring, retraining, and operational support.
- 3+ years of expertise utilizing MLOps, CI/CD pipelines, and automated model deployment processes including model monitoring, performance optimization, alerting, governance, and ongoing production support.
- Experience with Azure Machine Learning or equivalent cloud-based ML platforms (AWS SageMaker, Google Vertex AI, etc.)
- Experience with ML infrastructure, endpoint management, containers, and Kubernetes-based deployments combined with Python and SQL development skills.

📌 Senior Machine Learning Engineer - Affinity (Richmond Hill)
🏢 Affinity
📍 Richmond Hill