ML Engineer

Ework Group, founded in 2000 and listed on Nasdaq Stockholm, has around 13,000 independent professionals on assignment. The company provides total talent solutions partnering with clients in private and public sectors, focusing on IT/OT, R&D, Engineering, and Business Development to deliver sustainable value through holistic talent management.

• Bachelor's or master's degree in Computer Science, Operations Research, Mathematics, or Computing with 3-4 years of relevant experience
• Experience working with large data sets from varied sources
• Excellent programming skills in Python
• Experience with containerization (Docker) and Kubernetes
• Experience in cloud application development (Google Cloud Platform and Azure preferred)
• Problem-solving, debugging, troubleshooting, and designing solutions to complex technical issues
• Experience designing, building, and maintaining ETL workflows and data pipelines using orchestration tools like Apache Airflow or Kubeflow
• Proficiency with DevOps practices including CI/CD and Git-based workflows
• Comfortable working in Unix/Linux environments
• Hands-on experience designing, building, and supporting RESTful APIs
• Working knowledge of machine learning and deep learning concepts, supporting model deployment and monitoring

Good to have:

• Understanding of deep learning platforms such as Keras, TensorFlow, and/or PyTorch
• In-depth understanding of key machine learning and deep learning algorithms
• Knowledge of oilfield terminology and business practices
• Experience with IoT/Edge technology
• Recognized open-source contributions
• Familiarity with data engineering tools like Flink, Spark, Kafka
• Experience with big data stack (Spark, Hadoop, Storm, Hive, Pig) and NoSQL stores

• Script production-ready code
• Leverage GPU & CPU resources appropriately for ML workloads
• Architect, automate, and orchestrate DevOps/ML-Ops pipelines on cloud
• Craft reusable tools and frameworks to monitor, optimize, and maintain ML solutions
• Design and implement data engineering pipelines (ETL)
• Understand business problems and collaborate within the team to ensure scalability, business continuity, and appropriate turnaround time
• Conduct internal workshops and external meetups, participate in external conferences, and give talks
• Continuously evolve craft by keeping up to date with new developments in AI/ML and related technologies and upskilling as needed