We are looking for a Machine Learning Operations (MLOps) Engineer to build, deploy, and manage scalable ML pipelines and production systems.

This role focuses on bridging data science and engineering, ensuring reliable deployment, monitoring, and lifecycle management of machine learning models.

Key Responsibilities :

- Design, build, and maintain end-to-end ML pipelines (data ingestion, training, validation, deployment)

- Deploy and manage machine learning models in production environments

- Build and maintain CI/CD pipelines for ML workflows

- Develop scalable infrastructure for model training and inference

- Implement model monitoring, logging, and alerting systems (drift, performance, failures)

- Collaborate with Data Scientists to productionize ML models efficiently

- Automate workflows for data preprocessing, feature engineering, and retraining

- Optimize infrastructure for cost, performance, and scalability

- Ensure versioning of models, datasets, and experiments

- Troubleshoot production issues and ensure high system reliability

Required Skills & Qualifications :

- 2 - 6 years of experience in MLOps / DevOps / Backend Engineering with ML exposure

- Strong programming skills in Python

- Hands-on experience with ML lifecycle tools (MLflow, Kubeflow, Airflow, or similar)

- Experience with Docker and Kubernetes

- Familiarity with cloud platforms (AWS / GCP / Azure)

- Understanding of CI/CD tools (Jenkins, GitHub Actions, GitLab CI, etc.)

- Experience with data pipelines and orchestration tools

- Knowledge of REST APIs and microservices architecture

- Understanding of model monitoring, drift detection, and retraining strategies