Senior DevOps/MLOps/Cloud Engineer

WE'RE HIRING

Job Title: RQ00613 - Senior DevOps / MLOps / Cloud Engineer

8 month contract

Duration: 2026-04-13 - 2026-12-18

Work Status: Fully Remote

Location: Toronto, Ontario

Must-Have Skills:

• 10+ years of experience designing and implementing CI/CD pipelines using Azure DevOps, Git, and YAML
• 10+ years of experience deploying AI/ML models into production using automated pipelines
• 10+ years of experience implementing ML lifecycle management (training, validation, deployment, monitoring)
• 10+ years of experience designing and building data pipelines using Azure Data Factory, Databricks, and ADLS

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.

Required Skills & Qualifications

• Minimum 10+ years of IT experience with 10+ 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

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Nice to Have

• 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

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

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• 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