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 amp; 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 amp; evaluating the latest architecture patterns/technologies through rapid learning, conducting proof-of-concepts and creating prototype solutions.
Requirements
Must have:
- Bachelor's or Master's in Computer Science, Engineering, Mathematics, or related field.
- 4+ years’ hands-on experience in ML development.
- Minimum of 2 years of experience deploying and maintaining Data Science/Machine Learning applications.
- Good understanding of the fundamentals of machine learning
- Strong programming expertise in Python, PySpark/Scala.
- Expertise in crafting ML Models for high performance and scalability.
- Experience implementing feature engineering, inferencing pipelines, and real-time model predictions.
- Experience in ML Ops to measure and track model performance, experience working with MLFlow
- Experience with Spark or other distributed computing frameworks.
- Experience in ML platforms like SagaMaker and Kubeflow.
- Experience with pipeline orchestration tools such as Airflow.
- Experience in deploying models to cloud services like AWS, Azure, GCP, and Azure ML.
- Expertise in SQL and SQL DB's.
- Knowledgeable of core CS concepts such as common data structures and algorithms.
- Proficiency in English and strong communicative skills.
Nice to have:
- Understanding of DevOps, CI / CD, data security, and experience in designing on a cloud platform;
- Experience in data engineering in Big Data systems
Benefits
- 35 paid absence days per year for the work-life balance of each specialist + 1 additional day for each following year of cooperation with the company
- Up to 15 unused absence days can be added to income after 12 months of cooperation
- Health insurance
- Depreciation coverage for personal laptop usage for project needs
- Udemy courses of your choice
- Regular soft-skills training
- Excellence enters meetups