Role : Machine Learning Operations (ML Ops) Engineer

Role Overview :

The ML Ops Engineer is responsible for operationalizing machine learning models by building reliable, scalable, and governed pipelines that support the full ML lifecycle from development to production.

The role ensures models are deployed efficiently, monitored continuously, and integrated seamlessly into data and business platforms.

Key Responsibilities :

- Build and maintain end to end ML pipelines including data preparation, model training, deployment, and monitoring.

- Automate model deployment using CI/CD practices.

- Manage model versioning, experiment tracking, and reproducibility.

- Monitor model performance, data drift, and system reliability in production.

- Collaborate with data scientists and data engineers to productionize models.

- Implement governance, security, and access controls for ML workflows.

- Optimize infrastructure usage and ensure scalable model serving.

- Support incident troubleshooting and continuous improvement of ML systems.

Required Skills :

- Strong Python and SQL skills.

- Experience with ML platforms such as Databricks, MLflow, SageMaker.

- Knowledge of CI/CD tools and DevOps practices.

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

- Understanding of model lifecycle management and monitoring concepts.

- Familiarity with containerization and orchestration (Docker, Kubernetes) is a plus.

Preferred Experience :

- Experience working with large-scale data platforms and distributed processing.

- Exposure to feature stores, model governance, and automated retraining workflows