01Responsibilities
- Design, build, and maintain scalable ML pipelines.
- Deploy machine learning models using Docker, FastAPI, and AWS.
- Manage batch and real-time model inference.
- Build and maintain CI/CD pipelines for ML projects using GitHub Actions or similar tools.
- Manage the complete ML lifecycle using MLflow and Airflow.
- Monitor model performance, data drift, and production health.
- Optimize models to improve performance and accuracy.
- Work with cross-functional teams to deliver AI solutions.
- Participate in technical discussions and knowledge-sharing sessions.
- Follow best practices for scalable and production-ready AI applications.
Professional & Technical Skills:
- Robust experience in MLOps, Machine Learning, or NLP.
- Hands-on experience with MLflow and Airflow (Mandatory).
- Experience with Docker and FastAPI.
- Experience deploying ML models on cloud platforms.
- Knowledge of AWS and Multi-Cloud environments.
- Experience with ML frameworks such as TensorFlow or PyTorch.
- Strong understanding of CI/CD for ML applications.
- Experience with model monitoring, model versioning, and model deployment.
- Good understanding of Generative AI concepts.
- Strong communication and problem-solving skills.
Additional Information:
- Minimum 5+ years of experience in Machine Learning Operations (MLOps).
- Experience with MLflow and Airflow is mandatory.
- Experience with Generative AI will be an added advantage.
- Location: PAN INDIA
- 15 years of full-time education is mandatory. .
