Our customer is a leading consulting, software, and technology company servicing industries such as healthcare, private equity, technology, and more. It develops products that create value and deliver company results across critical areas of its business, including portfolio strategy, customer insights, research and development, operational and technology transformation, marketing strategy, and many more. Besides, the company is at the forefront of innovation, actively expanding its AI services to deliver cutting-edge solutions.
Responsibilities:
- Build, Refine and Use ML Engineering platforms and components.
- Scaling machine learning algorithms to work on massive data sets and strict SLAs.
- Build and orchestrate model pipelines, including feature engineering, inferencing, and continuous model training.
- Implement ML Ops, including model KPI measurements, tracking, model drift & model feedback loop.
- Collaborate with client-facing teams to understand the business context at a high level and contribute to technical requirement gathering. Implement basic features aligning with technical requirements.
- Write production-ready code that is easily testable, understood by other developers and accounts for edge cases and errors.
- Ensure the highest quality of deliverables by following architecture/design guidelines, coding best practices, and periodic design/code reviews.
- Write unit tests and higher level tests to handle expected edge cases and errors gracefully, as well as happy paths.
- Uses bug tracking, code review, version control and other tools to organize and deliver work.
- Participate in scrum calls and agile ceremonies and effectively communicate work progress, issues and dependencies.
- Consistently contribute to researching & evaluating the latest architecture patterns/technologies through rapid learning, conducting proof-of-concepts and creating prototype solutions.
Requirements
Must have: Nice to have:
Benefits