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Machine Learning & Data Engineer or Developer (m/f/d)

  • On-site
    • München, Bayern, Germany
  • Data DE

Job description

As a Senior Machine Learning & Data Engineer your aim is to develop consumer/downstream facing Data Analytics and ML applications using Open Source and Cloud tools. We focus on being able to support our customers at all stages in the development lifecycle of machine learning and data-driven applications. This means that your work will range from undertaking PoCs using state-of-the-art ML algorithms, to the expansion and improvement of production applications using the latest techniques of MLOps and DevOps. In the course of your work, you will collaborate with, learn from, and support the development of a highly skilled team of individuals with diverse background expertise. These areas of expertise range from state-of-the-art machine learning research to best practices in data engineering and software development.


Your tasks:

  • As part of our international team, you will support our clients during the successful development and implementation of complex solutions for Data Analytics and Machine learning applications.
  • Through the power of distributed open source and cloud technologies, you provide scalable, cost efficient and flexible solutions for our customers.
  • Use your software engineering expertise to provide clean and scalable solutions.
  • You are in regular contact with customers and stakeholders, and you communicate efficiently and professionally.
  • Use your technical experience and insight to help guide and shape our professional offering, to develop project opportunities with customers, and to support and mentor colleagues.
  • Use your ML and data engineering skills to implement and refine machine learning models and transform them into production applications for long-term customer benefit.
  • Work to iteratively improve and expand machine learning and data driven applications using the latest tools and methodologies in software lifecycle management.

Why us?

  • Voted Best Place to Work in Reply Germany in 2016, 2017, 2018 and a runner up in 2019 and 2020. Awarded Customer Satisfaction Award in 2019 and 2021.
  • Dedicated training days for your professional growth
  • Work in a great multicultural team, on interesting projects with new technologies.
  • Several teambuilding events throughout the year
  • Free German Lessons- to support you in becoming proficient in German.
  • Phone and laptop provided- necessary electronics are provided by the company.
  • Jobrad bike leasing scheme- lease a high-quality bike for personal and professional use with insurance and maintenance perks included.
  • Easy to get involved on topics of running a consulting business: HR, Sales, Marketing, Project Staffing.
  • Buddy Program: You will get a Buddy to help you get started.

Job requirements

  • >2 years of experience in the data engineering, and software and/or machine learning development.
  • A strong technical background, ideally a master’s degree in Computer Science or similar.
  • Proficiency in high level programming languages: Scala, Java, Python
  • Strong analytical and problem-solving skills
  • Excellent written and verbal communication skills.
  • Good German (B1) and English(C1 or equivalent) skills round off your profile

Nice to have:

  • Hands-on experience with Data Analytics technologies such as AWS Sagemaker, Spark, Elasticsearch
  • ML knowledge and experience in the fields of Natural Language Processing, Anomaly Detection and Computer Vision
  • Experience with one or more of the modern distributed Machine Learning and Deep Learning frameworks such as TensorFlow, PyTorch, MxNet, Caffe, and Keras.
  • Experience with one or more major public cloud providers (AWS, Azure, Google Cloud Platform).
  • Experience in modern Development practices (CI/CD, DevOps, GitOps, Agile, etc)
  • Expertise in MLOps frameworks and methodologies (Seldon, Kubeflow, MLflow)
  • Experience in containerization technologies and container orchestration (Kubernetes, Docker, Openshift).

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