Feature Platform MLOps Engineer

Nordic Investin Group Aktiebolag; Borås kommun; On-site; SEK 900,000 - 1,200,000; Full time

Job summary

Nordic Investin is recruiting a Feature Platform MLOps Engineer to align architecture, delivery and production behaviour across multiple ML teams. You will connect feature stores, Kubernetes and model registry while ensuring disciplined CI/CD and drift monitoring.

The role rewards independent judgement and a focus on operability in design. The position emphasizes collaboration with engineers, analysts and domain experts to deliver repeatable, measurable outcomes and clear decisions, while

Qualifications

• Experience delivering shared feature and model services for multiple machine-learning teams in a demanding environment.
• Strong working knowledge of feature stores and Kubernetes.
• Practical experience applying model registry and CI/CD.
• Ability to assess risk, priorities and trade-offs involving drift monitoring.
• Evidence of clear stakeholder communication and dependable end-to-end ownership.
• Professional English and the judgement to work with sensitive information.

Responsibilities

• Own the practical direction for shared feature and model services for multiple machine-learning teams.
• Translate goals and constraints into a clear approach for feature stores and Kubernetes.
• Establish useful standards, routines or artefacts around model registry.
• Coordinate stakeholders and dependencies affecting CI/CD.
• Use evidence and measurable outcomes to improve drift monitoring.
• Strengthen knowledge sharing, decision quality and sustainable ownership across the team.

Skills

Feature stores

Kubernetes

CI/CD

Stakeholder communication

English proficiency

Tools

Model registry

CI/CD pipelines

Job description

Nordic Investin is recruiting a Feature Platform MLOps Engineer on behalf of one of our partner companies. You want architecture, delivery and production behaviour to agree, and you turn difficult constraints into systems that teams can operate and improve.

The partner is strengthening its capability in shared feature and model services for multiple machine-learning teams. You will take responsibility for work that connects feature stores, Kubernetes and model registry, while ensuring that CI CD and drift monitoring receive appropriate discipline. The objective is progress that colleagues can use and leaders can evaluate.

The environment suits someone who tests assumptions early, documents trade-offs and treats operability as part of the design.

How you will work

You will work with engineers, analysts and domain specialists who look at the same information from different angles. The partner expects assumptions, datasets and evaluation methods to be visible. Solutions should be designed for repeatable delivery, monitored use and responsible change after deployment.

This role calls for independent judgement and the ability to raise the quality around you. You will be expected to recognise when a decision needs wider expertise, make trade-offs visible and move work forward without creating unnecessary process.

What you will do

• Own the practical direction for shared feature and model services for multiple machine-learning teams.

• Translate goals and constraints into a clear approach for feature stores and Kubernetes.

• Establish useful standards, routines or artefacts around model registry.

• Coordinate stakeholders and dependencies affecting CI CD.

• Use evidence and measurable outcomes to improve drift monitoring.

• Strengthen knowledge sharing, decision quality and sustainable ownership across the team.

What you will bring

• Relevant experience delivering shared feature and model services for multiple machine-learning teams in a demanding environment.

• Strong working knowledge of feature stores and Kubernetes.

• Practical experience applying model registry and CI CD.

• Ability to assess risk, priorities and trade-offs involving drift monitoring.

• Evidence of clear stakeholder communication and dependable end-to-end ownership.

• Professional English and the judgement to work with sensitive or business-critical information.

Experience that would add value

• Experience from data-rich product organisations.

• Exposure to cloud infrastructure in a cross-functional environment.

• Swedish language skills or experience working across Nordic organisations.

A background that can succeed here

Your background may be product engineering, platform work, enterprise technology or consulting. What matters is technical depth connected to user and business consequences. Direct experience of shared feature and model services for multiple machine-learning teams is valuable, while adjacent experience counts when it shows transferable judgement.

What makes the opportunity interesting

The attraction is meaningful ownership in an environment where sound judgement changes both the solution and the decisions around it. The focus on shared feature and model services for multiple machine-learning teams gives the role a clear mission and room to influence priorities and measures of success.

The exact partner, employment model, compensation, start date and working arrangement will be explained openly during the process. Nordic Investin will make sure you understand the opportunity before asking you to make a decision.

The recruitment conversation

Nordic Investin will focus on decisions you have made: the context, alternatives, result and what you would change today. You will also have room to test whether the role and partner environment are right for you.