Role Overview :

We are looking for an MLOps Engineer with strong hands-on experience in building and managing production-grade ML infrastructure, automation, deployment pipelines, and model lifecycle management.

Key Responsibilities :

- Build, automate, and manage end-to-end MLOps pipelines for machine learning model development, deployment, and monitoring.

- Develop and maintain data and ML workflows using Python and PySpark.

- Implement CI/CD pipelines for automated model testing, deployment, versioning, and release management.

- Containerize ML applications using Docker and manage deployments using Kubernetes or equivalent orchestration platforms.

- Design and manage scalable ML infrastructure on AWS, Azure, or GCP.

- Implement model deployment, version control, monitoring, logging, and automated retraining workflows.

- Establish processes for model validation, performance monitoring, and production reliability.

- Automate ML workflows and infrastructure provisioning using DevOps and Infrastructure-as-Code practices.

- Troubleshoot deployment, infrastructure, and production issues and implement appropriate improvements.

- Collaborate with Data Scientists, Data Engineers, Software Engineers, and DevOps teams to productionize and maintain ML models.

Required Skills :

- 4 - 9 years of experience in MLOps, ML Engineering, Data Engineering, or a closely related field.

- Strong hands-on proficiency in Python, PySpark, Git, and CI/CD.

- Practical experience with Docker and Kubernetes or equivalent container orchestration platforms.

- Strong understanding of the ML lifecycle, including model development, deployment, versioning, monitoring, and retraining.

- Experience with at least one major cloud platform: AWS, Azure, or GCP.

- Experience building automated ML/CI/CD pipelines and production deployment workflows.

- Understanding of cloud infrastructure, monitoring, logging, security, and scalability.

- Good understanding of DevOps practices and automation for ML workloads.