Machine Learning Engineer Staffice Global

5.0 - 8.0 years

13 - 18 Lacs

chennai

Hybrid

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).

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