The MLOps Engineer is responsible for automating, operationalizing and managing the machine learning lifecycle across all phases—training, evaluation, deployment, and monitoring. The role includes building CI/CD pipelines for ML workloads, enabling continuous training and deployment via Azure DevOps, maintaining feature and model registries and enforcing ML governance.

Key Responsibilities

Overall Responsibilities:

• Design and implement end-to-end MLOps pipelines for ML model lifecycle management.

• Collaborate with Data Scientists to streamline model experimentation and deployment workflows.

• Ensure reproducibility, scalability, and automation of ML systems.

• Maintain production-grade infrastructure with focus on availability, monitoring, and fault-tolerance.

• Establish model governance mechanisms including audit trails, access controls, and compliance frameworks.

• Enable secure and ethical AI practices aligned with FATE (Fairness, Accountability, Transparency, Ethics).

• Contribute to improving code quality, process automation, and DevOps culture in AI teams.

Technical Responsibilities:

• Develop and maintain CI/CD pipelines using Azure DevOps, Git, and Azure Pipelines.

• Implement model training, evaluation, and deployment workflows using MLFlow, DVC, and Airflow.

• Manage model versioning and experiment tracking, enabling reproducibility and lineage.

• Automate testing using frameworks like pytest, behave, and integrate SonarQube for code quality.

• Design and maintain deployment strategies: Blue-Green, Canary, and Shadow deployments.

• Configure monitoring and alerting pipelines using Prometheus, Grafana, and email triggers.

• Enable feedback loops and retraining mechanisms triggered by concept or data drift.

• Ensure rollback and recovery strategies for deployed models.

Tools & Technologies:

• Version Control & CI/CD: Git, Azure DevOps, Azure Pipelines, DVC

• Experiment Tracking & Registry: MLFlow, DVC, Azure ML

• Testing: pytest, behave, SonarQube

• Orchestration: Airflow, Azure Data Factory (optional)

• Monitoring & Alerting: Prometheus, Grafana, Cloudera tools, email notifications

• Deployment: Docker, Kubernetes (optional), Azure ML Endpoints

• Programming: Python, Bash, YAML, JSON

• Storage & Compute: Azure Blob, Cloudera, HDFS

Preferred Experience:

• 7-8+ years of hands-on experience in MLOps, DevOps, or ML Engineering roles.

• Proven experience deploying ML models at scale in production environments.

• Familiarity with monitoring model performance and automating drift detection and retraining workflows.

• Understanding of responsible AI concepts like fairness, transparency, and auditability.

Education & Certifications:

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field.

Certifications preferred:

• Azure DevOps Engineer Expert
• Certified MLOps Professional (TWiML, Coursera, or similar)
• Azure AI Engineer Associate (Optional)