Azure focused MLOps Engineer/Hybrid

Job Summary

We are looking for a senior-level MLOps engineer who can own the full lifecycle of ML models in a regulated, large-scale Azure enterprise environment.

• Core profile: Hands-on MLOps/ML platform engineer with strong Azure experience (Azure ML, Data Factory, Azure DevOps, Kubernetes).
• Technical must-haves: Python, Terraform, Docker/Kubernetes, CI/CD for ML, model versioning, monitoring, and production troubleshooting.
• Domain fit: Someone who has worked in banking, healthcare, or energy and understands regulated/sensitive data, compliance, and large-scale data pipelines.
• Working style: Cross-functional collaborator who can bridge Data Scientists, ML Engineers, and DevOps/Cloud teams.
• Mindset: Automation-first, production-focused, cost/performance-conscious, and current on MLOps trends.

Key Responsibilities:

• Design, implement, and maintain ML pipelines for seamless model deployment in Azure.
• Collaborate with Data Scientists, ML Engineers, and DevOps teams to streamline model training, testing, and deployment.
• Automate model monitoring, logging, and performance tuning in production.
• Work on scalable, secure, and resilient ML infrastructure in a complex environment.
• Implement CI/CD pipelines for ML models using Azure DevOps and other tools.
• Optimize model performance, cost, and efficiency while ensuring compliance with industry regulations.
• Troubleshoot and resolve issues related to ML model deployment, performance, and scaling.
• Stay updated on emerging MLOps tools, best practices, and industry trends.

Required Skills & Experience:

Hands-on experience with Azure ML, Azure Data Factory, Azure DevOps, and Kubernetes.

Strong expertise in MLOps frameworks, model versioning, and model monitoring.

Experience in banking, healthcare, or energy sectors handling regulated and sensitive data.

Proficiency in Python, Terraform, and containerization (Docker, Kubernetes).

Experience working in a complex enterprise environment with large datasets.

Knowledge of ML model development, training, and deployment workflows.

Ability to work cross-functionally with Data Scientists, Engineers, and Cloud teams.