ML Engineer

Responsibilities:

• Design, develop, train, and deploy machine learning models for real-world business problems.
• Work with large and complex datasets to perform data preprocessing, feature engineering, and model development.
• Build and optimize ML pipelines for training, evaluation, and inference.
• Develop scalable and reliable ML solutions for production environments.
• Evaluate model performance using appropriate metrics and continuously improve model accuracy and efficiency.
• Collaborate with data scientists, software engineers, product managers, and other stakeholders.
• Deploy and monitor machine learning models in production.
• Troubleshoot model performance, data quality, and production issues.
• Stay updated with the latest developments in machine learning, deep learning, and AI.

Requirements:

• 3-7 years of hands-on experience in machine learning / AI engineering.
• Strong programming skills in Python.
• Strong understanding of machine learning algorithms and concepts.
• Experience with Scikit-learn, Pandas, NumPy, and other relevant ML libraries.
• Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow.
• Good understanding of SQL and experience working with structured/unstructured data.
• Experience with model deployment and serving using tools/frameworks such as FastAPI, Flask, Docker, etc.
• Understanding of the ML lifecycle, model evaluation, experimentation, and monitoring.
• Familiarity with cloud platforms such as AWS, GCP, or Azure.
• Good understanding of software engineering principles, version control (Git), and writing production-quality code.