Duties description:
The person we are looking for will become part of DataScience and AI Competency Center working in AI Engineering team. The key duties are:
Building high-performing, scalable, enterprise-grade ML/AI applications in cloud environment
Working with Data Science, Data Engineering and Cloud teams to implement Machine Learning models into production
Practical and innovative implementations of ML/AI automation, for scale and efficiency
Design, delivery and management of industrialized processing pipelines
Defining and implementing best practices in ML models life cycle and ML operations
Implementing AI/MLOps frameworks and supporting Data Science teams in best practices
Gathering and applying knowledge on modern techniques, tools and frameworks in the area of ML Architecture and Operations
Gathering technical requirements & estimating planned work
Presenting solutions, concepts and results to internal and external clients
Being Technical Leader on ML projects, defining task, guidelines and evaluating results
Creating technical documentation
Supporting and growing junior engineers
Must have skills:
Good understanding of ML/AI concepts: types of algorithms, machine learning frameworks, model efficiency metrics, model life-cycle, AI architectures
Good understanding of Cloud concepts and architectures as well as working knowledge with selected cloud services, preferably GCP
Experience in programming ML algorithms and data processing pipelines using Python
At least 6-8 years of experience in production ready code development
Experience in designing and implementing data pipelines
Practical experience with implementing ML solutions on GCP Vertex.AI and/or Databricks
Good communication skills
Ability to work in team and support others
Taking responsibility for tasks and deliverables
Great problem-solving skills and critical thinking
Fluency in written and spoken English.
Nice to have skills & knowledge:
Practical experience with other programming languages: PySpark, Scala, R, Java
Practical experience with tools like AirFlow, ADF or Kubeflow
Good understanding of CI/CD and DevOps concepts, and experience in working with selected tools (preferably GitHub Actions, GitLab or Azure DevOps)
Experience in applying and/or defining software engineering best practices
Experience productization ML solutions using technologies like Docker/Kubernetes
