MLOps Engineer, Deep Learning

Insights from the team

You will join team DLFlow within our MLOps unit. Our MLOps teams create the systems and workflows that bring deep learning development together, enabling the end-to-end deep learning development; including training, evaluating, and deploying hardware-optimized models into the product code. Zenseact’s deep learning teams explore what needs to be solved and develop new ways to solve it. Our team helps turn those advances into a smooth, repeatable process for training, evaluating, and integrating models. We work closely with deep learning developers and other stakeholders across Zenseact to create an efficient AI factory.

The work sits at the intersection of deep learning and operations. We draw on ideas from CI/CD but adapt them to the needs of machine learning. Our focus is to make development more efficient and help deep learning engineers spend most of their time on deep learning itself. Automation, evaluation, and observability are central to what we deliver.

Your role and day-to-day tasks

Your role is to improve the way deep learning models move through development, evaluation, and delivery. You will create reliable workflows that make complex work easier to repeat, understand, and improve. You will also help teams decide which metrics matter and present results in a way that supports sound decisions.

A key part of the role is pushing the boundaries of how agentic AI can be integrated into the development cycle itself, rather than remaining a separate tool developers invoke as needed. You will explore new approaches, test them in practice, assess where they create real value, and turn the most promising ideas into dependable working methods.

Qualifications & experience

We believe you have practical knowledge of deep learning and understand what a typical development cycle looks like. You know the common pain points and can communicate effectively with deep learning engineers. Your interest goes beyond creating model improvements yourself. You are motivated by building the systems and procedures that help an entire development organization work effectively.

You follow new developments in deep learning and agentic AI, with a focus on what they can enable in practice. You combine curiosity with sound judgement and can recognise which emerging ideas have the potential to create real value. You are comfortable discussing ideas and trade-offs with stakeholders and working together to find practical ways forward.

Essential experience

• A master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.

• Practical experience with deep learning and a good understanding of its development workflows and challenges.

• Experience with automation, model evaluation, observability, or related MLOps practices.

• Understanding of CI/CD and devOps concepts and how they can be adapted to machine learning.

• Ability to turn technical results and metrics into clear, decision-friendly information.

• Ability to communicate and work effectively with engineers and other stakeholders.

Nice to have, but not crucial

• Hands-on experience integrating AI agents into software development workflows, beyond the use of coding assistants or other interactive AI tools.

• A track record of introducing new development practices across teams.

• Building automated training, testing, or evaluation pipelines for machine learning models.

• Developing observability or visualization tools for model performance.

This role may have access to sensitive information, trade secrets, and confidential data. As part of the recruitment process, the selected candidate might undergo a background check.

What’s in it for you

You will work on an engineering challenge that does not come with a ready-made answer. You will have room to bring in lessons from other environments while learning from people who work directly with deep learning every day.

Your contribution will give other engineers more space to focus on their core work. At the same time, you will have the opportunity to help shape how new technology, including agentic AI, is introduced thoughtfully into development processes. You will work across team boundaries and help ideas become reliable practices.