Description

Design, build, and deploy scalable machine learning models and MLOps pipelines.

This role is hybrid.

Responsibilities

• Design and develop machine learning and deep learning models for business use cases.
• Implement data preprocessing, feature engineering, and model training pipelines.
• Deploy and monitor models using MLOps frameworks like MLflow, Kubeflow, or SageMaker.
• Collaborate with engineering and product teams to translate requirements into scalable solutions.
• Ensure model governance, reproducibility, and performance tuning.

Required Skills

• 5+ years of experience in machine learning engineering.
• Strong proficiency in Python and R.
• Hands-on experience with TensorFlow, PyTorch, and Scikit-learn.
• Experience with MLOps tools: MLflow, Kubeflow, Airflow.
• Containerization and orchestration: Docker and Kubernetes.
• Cloud ML platforms: AWS SageMaker or GCP AI Platform.
• CI/CD, Git, and version control practices.
• Understanding of NLP, Computer Vision, or time series forecasting.

Preferred Skills

• Experience in productionizing ML models and building end-to-end ML pipelines.
• Familiarity with data lake architectures and real-time analytics.

Key Skills

Education

Any Graduate