MLOps Engineer

We are seeking a highly skilled and motivated MLOps Engineer to join team to develop, deploy, and maintain machine learning systems in a production environment. The role of an MLOps Engineer is critically important in the development, deployment, and maintenance of machine learning systems in a production environment. Previous experience with Python programming and knowledge of cloud platforms such as AWS is essential. 

What will be your key responsibilities:

The engineer will work on problem-solving within AWS and improving machine learning models using frameworks like TensorFlow, PyTorch, or scikit-learn. An important part of this role includes building robust data pipelines, designing, implementing, and maintaining infrastructure and tools for model training, testing, and deployment. Collaboration with various teams to optimize the machine learning workflows and ensure efficient and reliable machine learning operations is also key.

What experience should you have:

  • University degree in Computer Science, Data Science or a related field.
  • 3 years of experience as an MLOps Engineer, ML Engineer, or a similar role.
  • Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
  • Programming skills in languages such as Python or Java.
  • Hands-on experience with machine learning frameworks and libraries such as Pandas, TensorFlow, PyTorch, scikit-learn, or Keras.
  • Proficiency in building and managing scalable data pipelines using tools like Apache Kafka, Apache Airflow.
  • Solid understanding of DevOps principles and experience with CI/CD tools like Jenkins, Git, or Docker.
  • Familiarity with containerization technologies like Docker and orchestration tools like Kubernetes.
  • Knowledge of database systems, SQL, and big data technologies like Hadoop or Spark.
  • Strong problem-solving and analytical skills.
  • Ability to effectively collaborate with cross-functional teams.

What do you get in return:

  • 6 extra days off.
  • Referral bonus.
  • Benefit Plus budget.
  • Multisport card.
  • Financial contribution to pension plan.
  • Education support (certificates, courses, trainings).
  • Physiotherapist sessions once a week at the office.
  • Bonuses at significant life events.
  • Flexible working conditions.
  • Transparent approach and communication.
  • Support for your ideas.
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