DevOps Engineer

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About the Role

Azure DevOps Engineer

Azure Synapse, Databricks, Docker/Kubernetes, Terraform, and ML pipelines

This opportunity is perfect for DevOps professionals passionate about uniting data engineering, machine learning, and cloud operations to deliver impactful solutions.

Key Responsibilities

• Deploy and manage applications across

  Azure services

  (App Service, AKS, Functions, Logic Apps, Synapse, Databricks).
• Design and manage infrastructure using

  Terraform

  and Infrastructure-as-Code best practices.
• Implement networking, security, and access controls across Azure resources.
• Build and maintain

  CI/CD pipelines

  for backend applications and AI/ML workloads.
• Support

  data ingestion, feature engineering, training pipelines, and model deployment

  .
• Automate ML workflows using

  Databricks, Azure ML, and MLflow

  .
• Implement monitoring, logging, and alerting with

  Prometheus, Grafana, and Azure Monitor

  .

Collaboration & Support

• Collaborate with backend engineers to integrate APIs and cloud services.
• Partner with data scientists to move models into production environments.
• Troubleshoot performance, scaling, and security challenges.
• Create and maintain documentation for processes, pipelines, and deployment strategies.

Required Skills

• 5+ years of experience in

  Azure DevOps and Cloud Infrastructure

  .
• Strong expertise in

  CI/CD pipeline automation

  .
• Proficiency in

  Docker and Kubernetes (AKS)

  .
• Hands-on experience with

  Terraform and Infrastructure as Code

  .
• Experience with

  AI/ML pipeline automation, Databricks, Synapse, and Azure ML

  .
• Solid understanding of

  networking, security, and IAM in Azure

  .
• Strong scripting and automation skills (

  Python, Bash, PowerShell, YAML

  ).

Good to Have

• Knowledge of backend development in

  Python (Flask/FastAPI)

  for DevOps integration.
• Familiarity with

  MLOps best practices

  (data versioning, model monitoring, automated retraining).
• Certifications such as

  Azure Solutions Architect, DevOps Engineer Expert, or Data Engineer Associate

  .
• Exposure to

  AI/ML monitoring and governance frameworks

  .

Soft Skills & Attributes

• Strong problem-solving and debugging abilities with an automation-first mindset.
• Excellent communication skills for collaboration with developers, architects, and data scientists.
• Highly organized with strong documentation and knowledge-sharing habits.
• Flexible and adaptable to evolving project priorities and requirements.
• Proactive in identifying risks, bottlenecks, and opportunities for improvement.

Who Should Apply

• Professionals passionate about

  DevOps and MLOps convergence

  .
• Cloud engineers interested in working at the intersection of

  AI, data engineering, and backend development

  .
• Individuals motivated by

  automation, scalability, and production-grade solutions in Azure

  .