Senior ML engineer at Avenga
Berlin, Berlin, Germany -
Full Time


Start Date

Immediate

Expiry Date

08 Dec, 26

Salary

0.0

Posted On

09 Sep, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Yes

Skills

Industry

Information & Data Services

Description

About the job


This is us

At Avenga, we believe that human creativity empowers technology that matters. Operating globally, our 6000+ specialists provide a full spectrum of services, including business and tech advisory, enterprise solutions, CX, UX and Ul design, managed services, product development, and software development.This is the jobWe are looking for a Senior Machine Learning Engineer to join our team and work on developing accurate models of industrial equipment and system behavior.You will work with both experimental and observational data, iterating on ML approaches, evaluating results, and helping turn successful experiments into production-ready solutions.

  • This is youStrong experience in Machine Learning / Deep Learning and practical model development.
  • Experience working with real-world datasets, including data preprocessing and harmonization.
  • Solid understanding of deep learning architectures and the ability to adapt existing models to specific use cases.
  • Experience designing and evaluating ML experiments.
  • Strong Python and software engineering skills.
  • Nice-to-have skills:Experience with A/B testing and experimentation.
  • Experience with recommender systems or related ML domains.
  • Experience in industrial software, industrial engineering, or industrial applications.
  • This is your rolePreprocess, clean, harmonize, and prepare datasets for machine learning models.
  • Select, adapt, and potentially combine existing deep learning architectures to meet project goals.
  • Develop the necessary supporting infrastructure and integration (“glue”) around ML models.
  • Leverage LLMs to accelerate development and implementation where appropriate, including supervising LLM-generated solutions.
  • Design and run experiments to evaluate model performance and identify the most effective approaches.
  • Document experiments, results, and learnings to support decision-making and define the final production solution.
  • Collaborate with engineering and domain experts to translate industrial requirements into effective ML solutions.


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Responsibilities
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