We are looking for a highly skilled MLOps Engineer to design, build, and manage end‑to‑end machine learning pipelines and production‑grade ML infrastructure. This role focuses on scalable ML deployment, monitoring, automation, and lifecycle management, while bridging the gap between Data Science, Engineering, and DevOps teams.
If you have strong expertise in ML model deployment, CI/CD for ML, cloud platforms, and Kubernetes, this is an excellent opportunity to work on enterprise‑scale AI/ML systems.
Key Responsibilities-
- Design, build, and maintain CI/CD pipelines for machine learning models and data pipelines
Automate model training, validation, testing, deployment, and rollback
- Deploy ML models as REST APIs / microservices in production environments
Implement model monitoring, data drift detection, and performance tracking
- Manage and optimize cloud infrastructure (AWS / Azure / GCP) for scalability and cost efficiency
- Work closely with Data Scientists, ML Engineers, and Software Engineers to productionize ML solutions
- Ensure ML governance, security, compliance, reproducibility, and version control
- Maintain feature stores, model registries, and ML lifecycle tools. Troubleshoot and debug production ML systems.
Required Technical Skills-
- Python programming for ML and automation
- Machine Learning frameworks: TensorFlow, PyTorch, Scikit‑learn
- MLOps practices and ML lifecycle management
- Docker for containerization and Kubernetes for orchestration
- CI/CD tools: GitHub Actions, Jenkins, Argo CD
- Cloud platforms: AWS / Microsoft Azure / Google Cloud Platform
- ML tools: MLflow, DVC, Kubeflow
- Experiment tracking, data versioning, and model versioning
- ML monitoring & observability tools
- Understanding of feature engineering, feature stores, and pipeline orchestration
Experience Requirements-
- 5–8 years of experience in Software Engineering, Data Engineering, or ML Engineering
- 3+ years of hands‑on experience in MLOps / ML Platform Engineering
- Proven experience deploying and maintaining production ML systems
Preferred / Nice‑to‑Have Skills-
- Experience with large‑scale distributed ML systems
- Hands‑on exposure to Generative AI (GenAI) model deployment
- Knowledge of LLM optimization, inference scaling, and model serving
- Strong problem‑solving, debugging, and production support skills
