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Machine Learning Engineer
2 weeks ago
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.
Qualification
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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