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Pays:  France

Lyon, FR

Localisation détaillée: 
Type d'emploi:  Terme non défini
Poste à plein temps / temps partiel:  Temps plein

Machine Learning Engineer (W/M) - Lyon (Part Dieu)

Konecranes is a world-leading group of Lifting Businesses™, serving a broad range of customers. We are truly a global company with 18,000 employees at 600 locations in 50 countries. For over 80 years, we have been dedicated to improving the efficiency and performance of businesses in all types of industries. We believe that sustainable growth is a result of a strong responsible performance. Konecranes is committed to ensuring that all employees and job applicants are treated fairly in an environment which is free from any form of discrimination.


The DSL (Data Science Lab) of Konecranes was created in September 2019. This centralized team of 15 members gathers Data Scientists, Data Analysts and Machine Learning Experts that harvest value from Konecranes data.

Our target: build AI and machine learning solutions for all Konecranes business areas.

Konecranes will permit you to integrate into a worldwide group, see from the inside what happens in industries, and play a vital role in Konecranes Data Science transformation by developing production-ready code around the Data science solutions we are building.

Job description

The Data Science Lab team is looking for an enthusiastic and curious Machine Learning Engineer to complete our current roster.

We are seeking a team player with previous experience in MLOps.

Our Machine Learning Engineers mainly focus on deploying and monitoring Machine Learning models.

We mainly use the Microsoft Azure stack and Databricks.

We expect someone that will work with Data scientists and Data engineers daily to help them design and implement production-ready solutions.

We pay particular attention to desire to automate processes and to speed up production deployment.



Main missions

  • Develop automated deployment pipelines and improve the existing ones.
  • Contribute to our internal python package targeted at standardizing our development practices.
  • Collaborate with Data scientists to build monitoring solutions tailored to specific projects.
  • Write unit tests and integration tests for model serving.
  • Troubleshoot ML solutions.

Competencies and skills

Master’s degree in computer science, information systems or Machine Learning engineering related fields.

At least a previous significative experience as a Machine Learning Engineer.

Good understanding of machine learning notions such as neural networks, NLP, classification, regression, clustering, random forests, etc.

Proficient in Python, Apache Spark, and SQL skills.

Comfortable with Docker and Kubernetes.

Git knowledge.

Familiar with MLOps concepts.

Experience with a major cloud provider (Azure, AWS, GCP).

Fluent in English.

Curious about data science and AI.

Eager to work with the agile methodology (Scrum).


If you think you match, even partially, to this description, please contact us!


Why you will join us ?

At Konecranes, we believe that great customer experience is built on the people behind the Konecranes name – people committed to providing our customers with lifting equipment and services that lift their businesses. Everything we do, we do with passion and drive. 

We believe diversity drives business success and is the foundation for our growth. We welcome different backgrounds and skills that enrich our community, and we promote a place where we can ALL be ourselves. This is what makes Konecranes a unique place to work.


We support your personal and professional development through many perks

  • Career path
  • Health insurance
  • Lunch vouchers
  • Remote work
  • Flexible schedules
  • Works council advantages


You will join in an international Data Science team, which mission is to fuel Konecranes business growth and profitability by data-driven solutions. You will be a key player in Konecranes’ data-driven transformation and have the opportunity to work on various business challenges and data science projects for the company. We value our team member’s professional development and knowledge sharing.

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