Azure Senior Data Lead

Job Summary

Role – Sr.

MLOps Technical Lead:

Location – Anywhere in Canada Sell rate – CAD 130 to CAD 150 per hour

MLOps Technical Lead to drive the design, implementation, governance, and operationalization of enterprise AI/ML platforms built on** Microsoft Azure and Databricks**. The ideal candidate combines deep technical expertise in MLOps and cloud architecture with strong business analysis and stakeholder management capabilities.

Key Responsibilities

MLOps & Platform Leadership:

Define and implement enterprise MLOps strategy, standards, and best practices.Lead the design and deployment of end-to-end ML lifecycle solutions including:Data ingestion and preparation Feature engineering Model training and experimentation Model deployment Monitoring and retraining Establish CI/CD and CT (Continuous Training) pipelines for ML workloads.Drive model governance, reproducibility, versioning, lineage, and compliance.Implement observability frameworks for ML model performance, drift detection, and operational monitoring.Lead technical reviews and architecture governance for AI/ML initiatives.

Skill Requirements

Azure & Databricks Architecture:

Design scalable and secure cloud-native AI platforms leveraging: Azure DatabricksAzure Machine Learning Azure Data Factory Azure Data Lake Storage Gen2Azure DevOps / GitHub ActionsAzure Key Vault Azure Monitor / Log Analytics Azure Kubernetes Service (AKS)Microsoft Fabric Define reference architectures for batch, streaming, and real-time ML workloads.Ensure alignment with enterprise security, networking, and compliance standards.Optimize platform cost, performance, and reliability.Solution Architecture

**Translate business requirements into scalable AI/ML solution architectures.Develop architecture blueprints, solution designs, and technical roadmaps.Evaluate emerging technologies and recommend platform enhancements.Lead architecture workshops and design thinking sessions.Define integration patterns with enterprise systems and data platforms.**Business Analysis & Stakeholder Management

**Engage business stakeholders to understand strategic objectives and use cases.Conduct requirements gathering, gap analysis, and feasibility assessments.Define business outcomes, KPIs, and success metrics for AI initiatives.Create business cases and ROI assessments for AI/ML investments.Collaborate with Product Owners and business teams to prioritize AI capabilities.Facilitate communication between business and technical teams.**Team Leadership

Lead multidisciplinary teams comprising Data Engineers, Data Scientists, ML Engineers, and Cloud Engineers.Provide technical mentoring and coaching.Drive Agile delivery and DevOps practices.Establish engineering standards and operational excellence practices.

Other Requirements

1.Relevant certifications in Azure Data Factory, Azure Databricks, SQL, Oracle, or Python would be a plus.