ML Engineer

Job Summary

ML Engineer responsible for designing, developing, and deploying machine learning models for real-world business problems.

Responsibilities

• Design, develop, and deploy Machine Learning models for real-world business problems.
• Collect, clean, and pre process large datasets using Python, Pandas, and NumPy.
• Perform feature engineering to improve model accuracy and performance.
• Build and optimize classification, regression, and clustering models using Scikit-learn and other ML libraries.
• Conduct data analysis and apply statistical techniques to identify trends and insights.
• Train, validate, and evaluate machine learning models using appropriate performance metrics.
• Write efficient SQL queries to extract, transform, and analyze data from databases.
• Develop and integrate REST APIs using FastAPI for model serving and application integration.
• Deploy machine learning models to cloud platforms and monitor their performance.
• Implement experiment tracking and maintain model versioning for reproducibility.
• Collaborate with data analysts, software developers, and business teams to understand project requirements.

Requirements

• Python
• SQL
• Machine Learning
• Scikit-learn
• Pandas
• NumPy
• Feature Engineering
• Model Training
• Model Evaluation
• Classification
• Regression
• Clustering
• Statistics
• Git
• Docker
• REST APIs
• FastAPI
• Cloud
• Model Deployment and Experiment Tracking

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