We are seeking a 4+ years of experienced Machine Learning Operations (MLOps) Engineer to manage, deploy, and optimize machine learning models in production environments. The ideal candidate will collaborate closely with Data Scientists, Software Engineers, and DevOps teams to build secure, scalable, and automated ML infrastructure on Microsoft Azure. This role requires expertise in cloud infrastructure, CI/CD/CT pipelines, Kubernetes, monitoring, and MLOps best practices to ensure reliable and efficient machine learning operations.

Required Skills & Qualifications:

- 4 years to 6 years of experience in DevOps, MLOps, Cloud Engineering, or a related role.
- Strong understanding of Machine Learning concepts, MLOps principles, and cloud architecture.
- Hands-on experience with Microsoft Azure services, especially Azure Kubernetes Service (AKS).
- Proficiency in Python or scripting languages such as Shell or Groovy.
- Hands-on experience with Docker and Kubernetes.
- Experience building and managing CI/CD pipelines.
- Strong in Git and source code management best practices.
- Experience with monitoring, logging, and alerting tools.
- Familiarity with Microservices Architecture, REST APIs, and modern cloud-native development practices.
- Experience implementing data drift and model drift monitoring.




- Strong troubleshooting and problem-solving skills.
- Passion for continuous learning and adopting emerging technologies.

Key Responsibilities:

- Design, implement, and maintain secure, scalable, and highly available infrastructure on Microsoft Azure.
- Build, maintain, and optimize CI/CD/CT pipelines for Data Science and Machine Learning projects.
- Automate infrastructure provisioning, capacity planning, and demand forecasting.
- Develop tools and automation to improve platform availability, reliability, scalability, and performance.
- Configure and maintain platform monitoring, logging, alerting, and observability.
- Monitor system health, application performance, security controls, and cloud infrastructure costs.
- Implement and promote MLOps and DevOps best practices, tools, and automation across the organization.
- Manage Azure Kubernetes Service (AKS) environments for ML workloads.
- Implement and monitor data drift and model drift detection mechanisms.
- Drive continuous integration, continuous delivery, and continuous deployment initiatives.
- Troubleshoot production issues and ensure high platform reliability.
- Stay updated with emerging technologies and recommend creative solutions that add business value.

📌 DevOps with (MLOps) Engineer (Pune)
🏢 CGI
📍 Pune