Senior Machine Learning Engineer

• Design, develop, and deploy machine learning models and solutions to solve complex business problems. 

• Build and maintain production-grade ML pipelines for model training, evaluation, inference, and monitoring. 

• Perform data transformation and feature engineering to create reliable and scalable input features for machine learning models. 

• Implement model monitoring and drift detection frameworks to track data drift, feature drift, and model performance degradation in production. 

• Develop scalable APIs and ML services using frameworks such as FastAPI to integrate ML models into business applications. 

• Apply strong software engineering principles, including object-oriented programming, modular design, and code maintainability. 

• Implement unit and integration tests using frameworks such as pytest to ensure reliability and maintainability of ML systems. 

• Deploy and manage ML solutions in cloud environments, preferably Microsoft Azure. 

• Work with large-scale enterprise data platforms such as Snowflake and collaborate with data engineering teams to build reliable data pipelines. 

• Optimize model training and performance using distributed computing frameworks such as Ray and Dask. 

• Use Optuna or similar tools for hyperparameter tuning and model optimization. 

• Explore and implement Large Language Model (LLM) based solutions to address business problems such as knowledge retrieval, decision support, and workflow automation. 

• Participate in code reviews, system design discussions, and continuous improvement of engineering standards. 

• Collaborate closely with cross-functional teams including business, analytics, data engineering, and technology teams to deliver high-impact solutions. 

Qualifications 

• Master’s degree in Computer Science, Machine Learning, Data Science, Statistics, or a related quantitative field. 

• 4+ years of experience building and deploying machine learning models and systems in production environments. 

• Strong proficiency in Python for machine learning and software development. 

• Strong understanding of object-oriented programming (OOP) and software design principles. 

• Experience building APIs using frameworks such as FastAPI or similar Python web frameworks. 

• Experience implementing unit testing using frameworks such as pytest. 

• Strong understanding of data transformation, feature engineering, and feature pipeline development. 

• Experience implementing model monitoring, drift detection, and model performance tracking in production environments. 

• Experience working with cloud platforms, preferably Microsoft Azure. 

• Experience working with data lake or modern data platforms such as Snowflake. 

• Strong experience with machine learning frameworks such as scikit-learn, PyTorch, or TensorFlow. 

• Experience working with large-scale datasets and building scalable ML pipelines. 

• Familiarity with Large Language Models (LLMs) and their application to solve business problems.