Job Description

The purpose of this job is to implement machine learning models into production by utilizing state-of-the-art tools/algorithms and methodologies following DevOps and a test-driven development process.

1.  Deliver systematic approaches, integrating work into applications and tools with our influence, build and maintain the large-scale analytics infrastructure required for the AI projects, and integrate with external IT infrastructure/service to provide e2e solutions
2. Leverage an understanding of software architecture and software patterns to write scalable, maintainable, well-designed, and future-proof code
3.  Design, develop, and maintain the framework for analytical pipeline
4. Design machine learning systems and implement appropriate ML algorithms and tools in collaboration with the data science team
5.  Implement best MLOps practices for automation, monitoring, scaling, and reliability
6. Ensure all activities are in compliance with rules, regulations, policies, and procedures
7. Complete other duties as assigned

Qualifications

• Bachelor’s degree in Data Science, Computer Science, Engineering, Applied Mathematics, or any Quantitative field
• Master’s degree preferred
• Proficiency in at least one of the following programming languages: Python, C++, or Java
•  Minimum 5 years of experience in Data Science, Machine Learning, Software Engineering, or another quantitative discipline required
•  Minimum 5 years of experience with some, but not all the technologies mentioned in the Specialized Knowledge section
• Experience architecting machine learning pipelines, including designing, and improving infrastructure for ingesting, storing, and transforming data
•  Experience with the usage and implementation of CI/CD pipelines using Jenkins, GitHub Actions, TravisCI, or CircleCI
• Experience with scalable distributed systems hosted on cloud providers
• Experience implementing efficient machine learning pipelines at scale by utilizing distributed and/or GPU hardware optimizations methods
• Experience with data ETL processes and both SQL and noSQL databases and manipulating large structured or unstructured datasets for analysis
• Experience training machine learning models by applying feature engineering, model selection, sampling, and model evaluation strategies using Python frameworks such as scikit-learn, Pandas, Pytorch, NumPy, and PySpark
•  Experience developing and deploying scalable implementations of model training and model serving using any of the following technologies: MLFlow, AWS SageMaker, Triton Inference Server, ONNX RunTime, TorchScript, or TensorFlow RunTime
• Experience with version control systems (e.g., GitHub, GitLab)

Additional Information

• Nationwide Medical Plan/Dental/Vision
• 401(k) and Flexible Spending Accounts
• Adoption Assistance
• Tuition Reimbursement
• Weekly Pay
• All your information will be kept confidential according to EEO guidelines.