Description

You will develop end-to-end machine learning solutions, from data preprocessing and model training to deployment and monitoring.

This role is hybrid.

Responsibilities

• Develop and train ML models using supervised, unsupervised, and deep learning algorithms.
• Implement data preprocessing, feature engineering, model selection, and evaluation workflows.
• Deploy ML models using containerization and API frameworks.
• Manage data handling through SQL and NoSQL databases and ETL processes.
• Apply MLOps practices, including CI/CD pipelines and model monitoring.

Required Skills

• 5+ years of experience as an ML Engineer, Data Scientist, or AI Engineer.
• Strong proficiency in Python and libraries including TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy, and Keras.
• Expertise in NLP, Computer Vision, or Time Series Forecasting.
• Hands-on experience deploying models using Docker, Flask/FastAPI, or cloud services (AWS, Azure, GCP).
• Solid understanding of statistics, probability, and linear algebra.
• Experience working with large datasets and data pipelines.
• Familiarity with ML model deployment on AWS SageMaker or Azure ML Studio.
• Proficiency with Git for version control.

Preferred Skills

• TensorFlow Developer Certification or AWS Certified Machine Learning – Specialty.

Key Skills

Education

Any Graduate