Job Summary

We are looking for a talented and motivated ML Ops Engineer to provide technology leadership in building and maintaining robust machine learning operations (ML Ops) frameworks.
This role will focus on enabling scalable, reliable, and efficient deployment of AI/ML models, ensuring seamless integration between Data Science and production systems.  
You will collaborate closely with Data Scientists, ML Engineers, and DevOps teams to operationalize cutting-edge machine learning solutions and optimize the end-to-end ML lifecycle.

Job Requirements

- Design, implement, and maintain ML Ops pipelines for model training, deployment, monitoring, and retraining.  
- Collaborate with Data Science teams to transition models from research to production.  
- Automate workflows for data ingestion, feature engineering, and model evaluation.  
- Ensure scalability, reliability, and performance of deployed ML systems.  
- Implement monitoring tools to track model performance and detect drift.  
- Stay informed about best practices in ML Ops, Data Science, and cloud-native deployments.  

- 3–5 years of experience in Data Science and Machine Learning, with strong exposure to ML Ops practices.  
- Proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).  
- Experience with ML Ops tools (MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, etc.).  
- Strong understanding of CI/CD pipelines and containerization (Docker, Kubernetes).  
- Knowledge of cloud platforms (AWS, Azure, GCP) for ML deployment.  
- Familiarity with data processing tools (Spark, Pandas, etc.).  

Education

- Bachelor’s or Master’s degree in Computer Science, Data Science, or related field.  
- Experience with monitoring and logging tools for ML models.  
- Exposure to Generative AI model deployment (optional but nice to have).