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.

Experience
3-7 yrs