- Build and maintain end-to-end ML pipelines including data ingestion, preprocessing, training, and inference.

- Develop real-time ML/GenAI models and scalable APIs/services.

- Implement MLOps workflows using MLflow, CI/CD, Docker, Kubernetes, and Terraform.

- Work with cloud platforms (Azure, GCP) for model training, deployment, and monitoring.

- Integrate ML models with frontend & backend applications (React/Angular/Node).

- Optimize model performance, latency, and scalability for production environments.

- Work with vector databases and orchestration tools for advanced ML/GenAI use cases.

- Collaborate with data engineers, product teams, and stakeholders.

Required Skills :

- ML/AI Engineer with 58 years of experience in building end-to-end ML systems, real-time models, and scalable backend/ frontend solutions.

- Strong expertise in Python, PySpark, SQL, React.js / Angular / Node, and cloud ML platforms including Azure ML, GCP Vertex AI, BigQuery, Cloud Storage, AKS, Blob Storage, ADF, and Azure DevOps.

- Skilled in developing ML pipelines, data ingestion, preprocessing, training, inference, feature engineering, and MLOps using MLflow, CI/CD, Git, Docker, Kubernetes, and Terraform (IaC).

- Experience with deploying and monitoring ML/GenAI models, vector databases, real-time serving, orchestration tools, and building responsive UI interfaces for ML apps