MLOps Platform Engineer

Nordic Investin Group Aktiebolag; Stockholms kommun; On-site; SEK 800,000 - 1,100,000; Full time

Job summary

Nordic Investin Group Aktiebolag leads the recruitment for a senior MLOps Platform Engineer at a partner company. You will design and deliver reusable training and inference infrastructure, automate packaging and deployment, and build observability across ML systems.

You will collaborate with engineers, analysts and domain experts to ensure scalable, governable models with clear provenance and cost controls while driving responsible delivery and practical solutions.

Qualifications

• Production platform, DevOps or ML engineering experience.
• Strong Python and cloud infrastructure knowledge.
• Containers, Kubernetes and CI/CD.
• Experience with ML lifecycle tools such as MLflow or managed cloud services.
• Infrastructure as code and observability skills.
• Ability to design for both researchers and operators.

Responsibilities

• Create reusable training and inference infrastructure.
• Automate model packaging, validation and deployment.
• Build feature, registry and experiment tracking integrations.
• Implement monitoring for data and model behaviour.
• Manage compute, access and cost controls.
• Support teams adopting the platform through examples and documentation.

Skills

Production platform

DevOps

ML engineering

Python

Cloud infra

Containers

Kubernetes

CI/CD

ML lifecycle tools

Observability

IaC

Tools

MLflow

Managed cloud services

CI/CD tools

Terraform

Job description

One of Nordic Investin’s partner companies is looking for an MLOps Platform Engineer, with Nordic Investin leading the recruitment process. You want data scientists to experiment freely while production models remain reproducible, observable and governable.

The partner is scaling machine learning across several teams and needs a common platform for training, deployment and monitoring. You will build the paved paths that connect experiments with reliable services.

The partner wants data and AI work that can be operated, explained and improved after launch. Provenance, quality, access and evaluation belong in the solution from the beginning.

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.

As a senior colleague, you will own substantial outcomes and help others make stronger decisions. You are expected to recognise risk early, communicate it without drama and move work forward with practical alternatives. The role still includes hands on delivery; seniority here means broader judgement, not distance from the work.

What you will do

• Create reusable training and inference infrastructure.
• Automate model packaging, validation and deployment.
• Build feature, registry and experiment tracking integrations.
• Implement monitoring for data and model behaviour.
• Manage compute, access and cost controls.
• Support teams adopting the platform through examples and documentation.

What you will bring

• Production platform, DevOps or machine learning engineering experience.
• Strong Python and cloud infrastructure knowledge.
• Containers, Kubernetes and CI CD.
• Experience with ML lifecycle tools such as MLflow or managed cloud services.
• Infrastructure as code and observability skills.
• Ability to design for both researchers and operators.

Experience that would add value

• GPU workload orchestration.
• Feature stores and online inference.
• Governance in healthcare, finance or public services.

A background that can succeed here

Your route may be from platform engineering into machine learning or from data science into production systems. We value people who understand both experimentation speed and operational control.

What makes the opportunity interesting

The partner offers a platform at the point where good foundations will have lasting influence. You will multiply the effectiveness of several ML teams and see your work reflected in faster, safer releases.

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 context, expectations and decision path before you are asked to commit significant time.

The recruitment conversation

During the process, Nordic Investin will focus on concrete decisions you have made: the context you received, the alternatives you considered, the result you observed and what you would change today. You do not need every optional technology if your core experience transfers and you can explain how you would close the gap.