DevOps & MLops Engineer

Job Description : About the Role :

We are looking for a highly skilled DevOps & MLOps Engineer to design, implement, and manage scalable infrastructure and machine learning operations pipelines.

The ideal candidate will have hands-on experience in cloud platforms, CI/CD automation, containerization, infrastructure as code, and ML model deployment and monitoring.

You will work closely with Data Scientists, ML Engineers, and Software Development teams to streamline the end-to-end machine learning lifecycle.

Key Responsibilities : DevOps Responsibilities :

• Design, implement, and maintain cloud-native infrastructure on AWS, Azure, or GCP.

• Build and manage CI/CD pipelines for application and infrastructure deployments.

• Automate infrastructure provisioning using Infrastructure as Code (Terraform, CloudFormation, or similar tools).

• Manage containerized environments using Docker and Kubernetes.

• Monitor system performance, reliability, security, and cost optimization.

• Implement logging, monitoring, and alerting solutions using Prometheus, Grafana, ELK Stack, CloudWatch, or equivalent tools.

• Ensure high availability, scalability, and disaster recovery capabilities.

MLOps Responsibilities :

• Build and maintain ML pipelines for model training, validation, deployment, and monitoring.

• Automate model deployment workflows and manage model versioning.

• Implement ML lifecycle management using tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML.

• Collaborate with Data Science teams to operationalize machine learning models.

• Establish model monitoring, drift detection, and retraining mechanisms.

• Optimize model serving infrastructure for performance and scalability.

• Ensure governance, reproducibility, and compliance across ML workflows.

Required Skills & Qualifications :

• 3.55 years of experience in DevOps, Cloud Engineering, SRE, or MLOps roles.

• Strong experience with cloud platforms : AWS, Azure, or Google Cloud Platform.

• Hands-on experience with Docker and Kubernetes.

• Proficiency in CI/CD tools such as Jenkins, GitHub Actions, GitLab CI/CD, or Azure DevOps.

• Experience with Infrastructure as Code tools such as Terraform.

• Strong scripting skills in Python, Shell, or Bash.

• Experience with Linux system administration.