Job Description

Assignment: RQ00613 - DevOPS/Cloud Engineer - Senior

Requisition: RQ00613

Job Title: DevOPS/Cloud Engineer - Senior

Client: Supply Ontario

Start Date: 2026-04-13

End Date: 2026-12-18

Department: Operations

Office Location: Toronto

Business Days: 180.00

Location: Fully Remote

Public Sector Experience: No

Must Haves:

• 10+ years experience Design and implement robust CI/CD pipelines using Azure DevOps, Git, and YAML
• 10+ years experience Lead deployment of AI/ML models into production using automated pipelines
• 10+ years experience Implement model lifecycle management (training, validation, deployment, monitoring)
• 10+ years experience Design and build data pipelines using Azure Data Factory, Databricks, and ADLS

Description

• Experience: 10+ years in IT, 5+ years in DevOps / Data Engineering / MLOps

Role Overview

• We are seeking a highly skilled Senior DevOps / MLOps / Data Engineer to lead platform engineering, deployment automation, and AI/ML model deployment on Azure. This role emphasizes DevOps excellence, secure and cost-efficient deployments, and production-grade AI delivery, while supporting ML lifecycle management and scalable data engineering solutions.

Deliverables

• DevOps, Deployment & MLOps (Primary Focus)
  • Design and implement robust CI/CD pipelines using Azure DevOps, Git, and YAML
  • Manage end-to-end deployment pipelines across Dev, QA, and Prod environments
  • Lead deployment of AI/ML models into production using automated pipelines
  • Deploy, version, and manage AI models (batch and real-time inference)
  • Implement model lifecycle management (training, validation, deployment, monitoring)
  • Automate infrastructure provisioning using ARM/Bicep/Terraform
  • Manage Azure resources via Azure Portal, CLI, and scripting
  • Oversee cluster management (Databricks clusters, scaling, performance tuning)
  • Implement release strategies, versioning, rollback, and environment parameterization
  • Develop scalable inference endpoints and API-based model serving
  • Integrate AI services such as Azure AI Search and Azure AI Foundry
  • Integrate and deploy services using REST APIs
  • Monitor model performance, drift, and retraining strategies
  • Ensure platform reliability, monitoring, and incident response
• Data Engineering
  • Design and build data pipelines using Azure Data Factory, Databricks, and ADLS
  • Develop and optimize data models in Azure SQL, SQL Server, and Oracle
  • Implement ETL/ELT processes for large-scale data processing
  • Ensure data quality, governance, and performance optimization
  • Support medallion architecture (Bronze, Silver, Gold layers)
• Security & Compliance
  • Implement secure cloud architecture using RBAC, Managed Identities, and Azure Key Vault
  • Secure data pipelines, storage, and ML endpoints (encryption, network controls, private endpoints)
  • Ensure compliance with data protection standards (PII handling, auditability, governance)
  • Manage secrets, credentials, and access policies across environments
  • Optimize cloud costs across Databricks, storage, and compute resources
  • Implement cluster right-sizing, auto-scaling, and job vs all-purpose cluster strategies
  • Monitor usage and enforce cost governance across environments
  • Recommend architecture improvements for cost-performance balance
• API & Integration
  • Design and build scalable REST API layers for data access and model inference
  • Develop and manage API-based integration patterns across internal systems and external vendors
  • Enable real-time and batch integration with downstream applications
  • Implement API security (authentication, throttling, versioning)
  • Support integration with event-driven systems and messaging frameworks

Required Skills & Qualifications

• Minimum 10+ years of IT experience with 5+ years in DevOps / MLOps / Data Engineering
• Strong expertise in:
• Azure ecosystem (ADF, Databricks, ADLS, Azure SQL)
• CI/CD pipelines, Git, and YAML-based deployments
• Infrastructure as Code (ARM, Bicep, Terraform)
• SQL and relational databases (Azure SQL, Oracle)
• REST API development and integration
• Proven experience in:
  • Deploying AI/ML models to production environments
  • End-to-end deployment pipelines and release management
  • Cluster management and optimization

Additional Terms

Nice to Have / Bonus

• Experience with Azure AI Search and Azure AI Foundry
• Experience with event-driven architecture (Event Grid, Service Bus)
• Exposure to streaming platforms (Kafka, Event Hubs)
• Knowledge of containerization (Docker, Kubernetes, AKS)
• Experience with LLMs / Generative AI pipelines and prompt orchestration
• Familiarity with data governance and medallion architecture

Soft Skills

• Strong problem-solving and troubleshooting abilities
• Ability to work across DevOps, Data, and ML teams
• Excellent communication and documentation skills
• Leadership and mentoring capabilities

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