Build the backbone of our AI solutions

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.

In this role you will:

• 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.

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.

What We Offer:

• A unique and pivotal role where you will work at the intersection of Data Engineering, DevOps, MLOps, and Privacy.

• The opportunity to shape and build the future platform for responsible AI and data in a growing company.

• Work on complex and meaningful challenges in telecom security, where privacy and ethics are central.

• A competent, inclusive, and informal work environment with a strong focus on learning and development.

• High degree of autonomy and influence on technology choices and solutions.

• Competitive compensation (salary, pension, insurance).

• A nice workplace in Grimstad (Sørlandets Teknologipark) with flexibility for partial remote work.