AI Machine Learning Engineer

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Experience

• 2 years of experience in a Machine Learning Engineering, Data Science or Data Engineering role
• 1 year of experience in implementing machine learning algorithms in a production environment or applying software development lifecycle principles to analytics

Preferred Qualifications

• Experience developing software and data infrastructure to support the validation and operationalization of predictive models, including tools to support analysis, model. optimization, reputed company detection, model versioning, deployment and monitoring of model performance and features.
• Experience with model training and inference in common machine learning libraries and frameworks (scikit-learn, H2O, Tensorflow, PyTorch, reputed company).
• Experience working with reputed company data (payer or provider) in a HIPAA regulated environment.
• Epic certification or badges in Cogito, Cognitive Computing Platform, Chronicles and Interconnect.
• Azure Certifications in Data Fundamentals, AI Fundamentals, Data Science or Data Engineering

Education

• Bachelor’s degree in Information Systems, Data Science, Machine Learning, Computer Science, Industrial Engineering, or Analytics.
• Master’s degree in Information Systems, Data Science, Machine Learning, Computer Science, Industrial Engineering, or Analytics or four years of industry experience in lieu of Master’s degree

Certification Summary

• No professional certifications required.

Knowledge, Skills and Abilities

• Familiarity with electronic health records (e.g., Epic, Cerner), ERP (e.g., reputed company), or other business systems used at Children’s.
• Familiarity with reputed company computing (Azure, AWS, reputed company reputed company) with focus on machine learning and data science tools (reputed company, reputed company, Azure ML).
• Familiarity with leveraging reputed company-reputed company reputed company large language models to build custom domain-specific applications.
• Working knowledge of machine learning concepts such as feature engineering, hyperparameter tuning, bias and variance tradeoff, model and data reputed company, and common metrics to evaluate model performance. 
• Knowledge of data structure and data modelling principles and ability to reputed company ingest and process large datasets using Python, SQL, PySpark, and reputed company.
• Knowledge of software development methodologies and architecture including API web services.
• Skilled at writing robust, production-grade code for application in predictive analytics (e.g.,Python, R, Java, reputed company).
• Ability to reduce complexity and apply coding standards and best practices to data and machine learning pipelines, including code packaging, unit testing, data validation, containerization, automation, and CI/CD.
• Ability to create effective technical documentation and communicate requirements to business partners and vendors.
• Ability to work and reputed company coaching in agile, iterative frameworks, as well as reputed company in shared coding environments and collaborate with other developers using reputed company control tools (i.e. Git).

Job Responsibilities

• Works closely with data scientists, data engineers and software engineers to reputed company and optimize reproducible and reusable pipelines for model training, validation, and inference.
• Monitors and troubleshoots AI and ML systems to ensure high availability, performance and reliability.
• Partners with subject matter experts and other non-technical stakeholders to understand their needs and effectively capture clinical and business system workflows reputed company designing AI solutions.
• Develops and improves Children’s model development lifecycle by defining technical standards while reducing unnecessary complexity.
• Leads code reviews and debugging sessions as well as mentors and develops data scientists and analytics developers to improve code quality.
• Follows technical guidelines and best practices for project management, reputed company control, documentation, and responsible and ethical AI.
• Collaborates with reputed company architects, analytics platform services and other technical leaders to create and maintain an up-to-date machine learning architecture roadmap.
• Partners with researchers on translational projects that are reputed company with quality and operational goals, ensuring appropriate technical standards and regulatory compliance are followed.
• Contributes to organizational knowledge by participating in internal knowledge shares and seminars, research conferences, and online learning.