Department : Support & Security

Role Overview : -

As an MLOps Engineer, you will bridge the gap between research and production by building robust, scalable pipelines for AI model deployment and monitoring. You'll ensure models are reliable, secure, and production-ready.

Key Responsibilities :

- Develop and manage ML pipelines, CI/CD workflows, and cloud-based infrastructure.

- Monitor, maintain, and optimize deployed models.

- Ensure reproducibility, scalability, and security of AI systems.

- Collaborate with AI engineers and data teams to move models from research to production.

- Implement logging, monitoring, and rollback strategies.

Requirements :

- Bachelor's degree in Computer Science, Engineering, or similar.

- Strong skills in Kubernetes, Docker, MLflow, TensorFlow Serving, or TorchServe.

- Experience with cloud platforms (AWS, Azure, GCP).

- Knowledge of data versioning, experiment tracking, and pipeline orchestration (Airflow, Prefect).

- Understanding of DevOps principles applied to AI/ML