MLOPS Engineer (NCS/Job/ 1981)

We are looking for an experienced MLOps Engineer with strong foundations in DevOps practices and hands-on expertise in Azure cloud services. The ideal candidate will design, automate, and optimize ML model development and deployment pipelines, ensuring scalability, reliability, and security across the machine learning lifecycle.

Key Responsibilities

• Design, build, and maintain end-to-end MLOps pipelines for ML model training, testing, and deployment.
• Collaborate with Data Scientists to productionize ML models in Azure ML and Azure Databricks.
• Implement CI/CD pipelines for ML workflows using Azure DevOps, GitHub Actions, or Jenkins.
• Automate infrastructure provisioning using IaC tools (Terraform, ARM templates, or Bicep).
• Monitor and manage deployed models using Azure Monitor, Application Insights, and MLflow.
• Implement best practices in model versioning, model registry, experiment tracking, and artifact management.
• Ensure security, compliance, and cost optimization of ML solutions deployed on Azure.
• Work with cross-functional teams (Data Engineers, DevOps Engineers, Data Scientists) to streamline ML delivery.
• Develop monitoring/alerting for ML model drift, data drift, and performance degradation.

Required Skills

• Programming: Python (must), SQL;
• MLOps/DevOps Tools: MLflow, Azure DevOps, GitHub Actions, Docker, Kubernetes (AKS).
• Azure Services: Azure ML, Azure Databricks, Azure Data Factory, Azure Storage, Azure Functions, Azure Event Hubs.
• CI/CD: Experience designing pipelines for ML workflows.
• IaC: Terraform, ARM templates, or Bicep.
• Data Handling: Experience with Azure Data Lake, Blob Storage, and Synapse Analytics.
• Monitoring & Logging: Azure Monitor, Prometheus/Grafana, Application Insights.
• Strong knowledge of ML lifecycle (data preprocessing, model training, deployment, monitoring).

Preferred Qualifications

• Experience with Azure Kubernetes Service (AKS) for scalable model deployment.
• Knowledge of feature stores and distributed training frameworks.
• Familiarity with RAG (Retrieval Augmented Generation) pipelines and LLMOps.
• Azure certifications such as Azure AI Engineer Associate, Azure Data Scientist Associate, or Azure DevOps Engineer Expert.

Soft Skills

• Strong problem-solving and debugging skills.
• Ability to work in an Agile/DevOps environment.
• Excellent communication and collaboration skills.
• Mindset for automation, scalability, and reliability.

Education

• Bachelor’s or Master’s in Computer Science, Data Science, AI/ML, or related fields.

Must have

• git
• azure devops
• docker
• mlflow
• model registry
• kubernetes/AKS
• azure pipelines/Github actions
• terraform

Good to have

• jenkins
• grafana, prometheus
• ELK stack
• automl
• azure monitor
• onnx/pytorch/tensorflow/tflite
• Kubeflow