Senior Engineer, AI & ML (multiple positions)
Role details
Job location
Tech stack
Job description
- Responsible for providing reliable and scalable machine-learning capabilities across the organization.
- Partner with Data Scientists to design and produce machine learning models. Processes include testing and monitoring applications and front-end experiences.
- Convert machine learning models into mission-critical capabilities that drive customer experience.
- Research and discover new opportunities to shape products, tools, and technologies to improve business performance.
Requirements
Master's degree in Information Systems, Information Technology, Computer Science, Operations (any), Management (any), Engineering (any), Analytics (any), Business (any), Language (any), Telecom, Math, Physics, Electrical/Electronics, or a related field of study, AND Three (3) years of experience in the job offered or related occupation in which the required experience was gained. In lieu of a Master's degree in Information Systems, Information Technology, Computer Science, Operations (any), Management (any), Engineering (any), Analytics (any), Business (any), Language (any), Telecom, Math, Physics, Electrical/Electronics, or a related field of study, AND Three (3) years of experience, the employer will also accept a Bachelor's degree in Information Systems, Information Technology, Computer Science, Operations (any), Management (any), Engineering (any), Analytics (any), Business (any), Language (any), Telecom, Math, Physics, Electrical/Electronics, or a related field of study, AND Five (5) years of experience in the job offered or related occupation in which the required experience was gained. Must also have demonstrated experience with the following: Designing and Developing on distributed architectures (microservices or Kubernetes); End-to-end design and development of scalable services for monitoring and product support services; Python programming and coding; Building enterprise-level solutions with Microsoft Azure or equivalent cloud technologies; DevOps practices, testing frameworks, and CI/CD pipelines; Object-oriented programing language (C# or Java); Machine-learning and AIaaS; MLOps and industry-standard machine-learning Python libraries; Model Development and Deployment (MLFlow, Azure ML, or Sage Maker); and Data Science Libraries and Algorithms (TensorFlow, Scikit, or PyTorch).
