Our AI team develops advanced models, including those combating telecom fraud. To enhance quality, speed, and reliability across the delivery cycle -from data experimentation to stable model deployment in production- we need to establish a robust, modern, and privacy-focused data and MLOps foundation.
As our new Data Engineer, you will play a pivotal role. You will not only build data pipelines but also operationalize our AI models securely and responsibly.

WHO YOU ARE?

We are looking for someone who has:

• Relevant higher education in IT/data/engineering or several years of practical experience as a Data Engineer, preferably with exposure to MLOps or machine learning model operations.
• Strong technical expertise in Python.
• Experience building reliable data pipelines to support ML workflows (training and/or inference). Experience with specific tools like Spark, Kafka, Airflow, or dbt is beneficial but secondary to understanding pipeline principles for ML.
• Proficient in Linux environments and preferably experience with on-premises installations.
• A genuine desire to automate processes and build robust systems. Experience with container technology (Docker) and orchestration (Kubernetes, including Helm) is important for this role.
• Strong interest in—and ideally practical experience with—DataOps/MLOps principles and tools.
• A strong awareness of—and preferably experience with—handling sensitive data and privacy (e.g., GDPR). You understand the importance of security and ‘privacy by design’.
• Ability to communicate effectively technically and bridge between different disciplines. You speak ‘enough AI/ML’ to understand data scientists’ needs and translate them into robust infrastructure and processes.
• We would be delighted if you also have experience with: Monitoring tools (Prometheus/Grafana, MLFlow++), specific privacy-enhancing techniques, or telecom sector data.
• Structured, solution-oriented, and with good collaboration skills.
• Fluent in both spoken and written Norwegian and English.

• Design, develop, and operate scalable and efficient data pipelines supporting both ML model training and inference, with built-in privacy mechanisms.
• Implement and enhance DataOps/MLOps practices, including CI/CD for machine learning, automated testing, version control, and model monitoring in production.
• Ensure privacy and data security (’privacy by design’) throughout data processing, aligning with GDPR and customer requirements, especially in challenging on-premises environments.
• Work on automation of deployment, configuration, and monitoring of both data and ML solutions, leveraging technologies like Kubernetes and Helm, with security as a foundational requirement.
• Act as a key technical bridge between the AI team (data scientists) and other engineering resources.
• Ensure data quality, governance, and compliance (including privacy) throughout the ML lifecycle.
• Actively contribute to technology and architecture choices for our data and MLOps platforms
• Be instrumental in establishing a smooth, robust, scalable, and privacy-friendly process for AI development and operations at Utel.