Engineer

at  CLEARPEAKS

ClearPeaks is a specialist consulting firm delivering services and solutions in “Everything Data” – Business Intelligence, Advanced Analytics, Big Data & Cloud, and Web & Mobile Applications. Founded in 2000, we have been a trusted partner to our customers in over 15 industry verticals and functional areas, with operations spanning Europe, Middle East, the United States, and Africa.
ClearPeaks is part of a strategic alliance with synvert, a group of six successful full-service Data & Analytics (D&A) consulting firms, with a clear goal to become one of EMEA’s largest D&A consulting companies.
Our services are based on the latest market-leading enterprise technology platforms, and delivered by a dynamic team of expert consultants. Our strength lies in our ability to efficiently deliver customer insight and value, gained through our decades of experience with real-world challenges.
As a Data Engineer with a focus on machine learning integration, you will design, implement, and manage data pipelines in Dataiku, leveraging automation, testing, and monitoring best practices. Additionally, you’ll implement and embed machine learning models within data services, ensuring scalability, performance, and reliability through MLOps practices.
At ClearPeaks, there are endless opportunities to get involved in different projects bringing innovative ideas while being part of leading-edge teams who are always innovating and evolving in the Data Management field.

REQUIREMENTS

• A degree in Computer Science or Telecommunication Engineering, Informatics, Statistics or related degree.
• Proven experience with Python and Jupyter notebooks for data engineering and machine learning tasks.
• Familiarity with Dataiku and experience with implementing data pipelines in the platform.
• Strong knowledge of general machine learning principles, including similarity search, recommendation engines, and supervised learning.
• Ability to implement API services using libraries such as Pydantic and FastAPI.
• Solid understanding of MLOps practices and model lifecycle management.
• Familiarity with best practices in production code, object-oriented programming, and software architectures.

• Design, implement, and maintain data pipelines in Dataiku with a focus on automation, monitoring, and testing.
• Develop and integrate Python API services within Dataiku, following production-grade best practices.
• Embed machine learning models into data services and optimize them for performance.
• Perform model testing, evaluation, and continuous monitoring to ensure optimal model performance.
• Collaborate with stakeholders to gather requirements, analyse data needs, and provide solutions using Dataiku.
• Actively participate in MLOps practices, from deployment to monitoring and model lifecycle management.