Senior Machine Learning Engineer - Python/SQL

Description :

Core Responsibilities :

- Build and maintain end-to-end machine learning systems, including data ingestion, preprocessing, feature engineering, model training, and deployment.

- Develop scalable ML pipelines and workflows using Python, PySpark, SQL, and cloud-native ML platforms such as Azure ML and GCP Vertex AI.

- Implement and manage MLOps practices using MLflow, CI/CD pipelines, Git, Docker, Kubernetes, and Terraform to ensure reliable model delivery.

- Deploy, scale, and monitor ML and GenAI models in production, ensuring performance, accuracy, and real-time availability.

- Design and optimize model serving architectures using vector databases, orchestration frameworks, and real-time inference tools.

- Build responsive and intuitive frontend interfaces for ML applications using React.js or Angular, and scalable backend services using Node.js.

- Collaborate with cross-functional teams to translate business problems into ML/AI solutions and provide technical leadership in solution design.

- Ensure best practices in model governance, reproducibility, security, and compliance across the ML lifecycle.

- Optimize cloud resources and manage data storage using BigQuery, Cloud Storage, AKS, Blob Storage, and related services.

- Continuously experiment, evaluate, and refine ML models and systems to improve accuracy, efficiency, and user experience.

Mandatory Skills :

- ML/AI Engineer with 58 years of experience in building end-to-end ML systems, real-time models, and scalable backend/ frontend solutions and overall 8+

- 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