ML Engineer

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Job Title: Machine Learning Engineer

Experience Level: 5 - 8 Years Location: Chennai

About the Role: We are seeking a highly skilled and experienced Machine Learning Engineer to join our innovative team within the U.S. healthcare payer sector. You will be instrumental in designing, developing, and deploying cutting-edge machine learning models that address critical business challenges, improve operational efficiency, and enhance member outcomes. This role demands a strong understanding of ML principles, robust software engineering practices, and familiarity with the unique complexities of healthcare data.

Responsibilities:

• Design, develop, and implement scalable machine learning models and algorithms to solve complex problems related to claims processing, fraud detection, risk stratification, member engagement, and predictive analytics within the payer landscape.
  
• Collaborate closely with data scientists, product managers, and other engineering teams to translate business requirements into technical specifications and deliver end-to-end ML solutions.
  
• Develop and optimize ML model training pipelines, ensuring data quality, feature engineering, and efficient model iteration.
  
• Conduct rigorous model evaluation, hyperparameter tuning, and performance optimization using statistical analysis and best practices.
  
• Integrate ML models into existing applications and systems, ensuring seamless deployment and operation.
  
• Write clean, well-documented, and production-ready code, adhering to high software engineering standards.
  
• Participate in code reviews, contribute to architectural discussions, and mentor junior engineers.
  
• Stay abreast of the latest advancements in machine learning, healthcare technology, and industry best practices, actively proposing innovative solutions.
  
• Ensure all ML solutions comply with relevant healthcare regulations and data privacy standards (e.g., HIPAA).

Required Technical Skills:

• 
  Programming Language: Expert proficiency in Python.
  
• Machine Learning Libraries: Strong experience with PyTorch and scikit-learn.
  
• Version Control: Proficient with Git and GitHub.
  
• Testing: Solid understanding and experience with Python unittest framework and Pytest for unit, integration, and API testing.
  
• Deployment: Hands-on experience with Dockerized deployment on AWS or Azure cloud platforms.
  
• CI/CD: Experience with CI/CD pipelines using AWS CodePipeline or similar alternatives (e.g., Jenkins, GitLab CI).
  
• Cloud Platforms: Experience with AWS or Azure services relevant to ML workloads (e.g., Sagemaker, EC2, S3, Azure ML, Azure Functions).