Machine Learning Engineer | SEB, Solna

SEB group; Sweden; Hybrid; SEK 372,786 - 559,179; Full time

Benefits offered by this job

Agile working environment

Extensive training and learning opportunities

Hybrid work model

Job summary

SEB group in Sweden is looking for a Machine Learning Engineer who will design and operate end-to-end ML pipelines, focusing on model lifecycle management and secure deployment. The role requires strong collaboration skills and practical experience with modern ML frameworks. SEB group offers a hybrid work model, extensive training opportunities, and a progressive culture that values diversity and inclusion. If you're passionate about AI and innovation, this is the perfect opportunity for you.

Qualifications

• Strong foundation in machine learning concepts and model lifecycle management.
• Practical experience with modern ML frameworks and LLMs.
• Expertise in designing and operating end-to-end ML pipelines.

Responsibilities

• Building end-to-end ML pipelines, covering training, validation, and deployment.
• Implementing MLOps practices, including drift detection and model versioning.
• Exposing models as secure APIs and understanding AI risks.

Skills

Machine learning concepts

End-to-end ML lifecycle management

Modern ML frameworks

MLOps practices

Secure model serving

Collaboration and communication skills

Job description

Drive the future of AI with us! If you're passionate about end-to-end ML lifecycle management, secure model deployment, and enabling innovation through reusable, scalable services, this role is for you.

SEB is more than just a bank with an IT function. We are a progressive tech company powered by innovation and fuelled by collaboration. From COBOL to AI and everything in between, we offer you an impressive tech stack and several technology paths. Together we’re innovating for a sustainable society and building the tech arena of the future, a welcoming space for everyone. Are you ready to take on a new challenge? If so, you’ll be interested in this opportunity!

About the role

As a Machine Learning Engineer, you will build upon a strong foundation in machine learning concepts, model evaluation, and full model lifecycle management. Your practical experience with modern ML frameworks and LLMs will be crucial in deciding when and how to productionize models. You’ll design and operate end-to-end ML pipelines, covering training, validation, deployment, monitoring, and rollback, embracing MLOps practices like model versioning and drift detection. We expect you to expose models as secure, versioned APIs, demonstrating a service‑oriented mindset for reusable and scalable services, while understanding secure model serving and AI system risks.

Qualifications

• A strong foundation in machine learning concepts, model evaluation, and full model lifecycle management.
• Practical experience with modern ML frameworks and LLMs, including productionising models.
• Expertise in designing and operating end-to-end ML pipelines and MLOps practices such as experiment tracking and drift detection.
• Understanding of secure model serving, including authentication, tenant isolation, and awareness of AI system risks such as data leakage.
• A platform and product mindset with strong collaboration and communication skills, able to explain ML trade‑offs clearly.
• Strong social and interpersonal skills, enabling you to build trust, work effectively across teams, and collaborate with stakeholders from diverse technical and non‑technical backgrounds.

What we offer

• Agile and modern ways of working
• A flat hierarchy and openness to share ideas, opinions and points of view
• Extensive training and learning opportunities
• Hybrid work model, meaning remote work is possible up to 2 days per week
• Innovative company at the forefront of technology

It is our fundamental belief that inclusion and diversity is crucial for our future success. We strive to have an inclusive, value‑driven culture where employees feel valued, respected, and involved irrespective of who they are, what they believe or where they come from.