Information Technology Infrastructure Engineer

AddanEx International; Stockholms kommun; On-site; SEK 900,000 - 1,200,000; Full time

Job summary

AddanEx International in Stockholm, Sweden is seeking a Cloud Infrastructure Engineer / AI/ML Engineer to design, deploy, and operate AI-enabled solutions across diverse use cases. You will work with Data Scientists, Data Engineers, and stakeholders to translate requirements into scalable production systems on Google Cloud Platform.

The role combines cloud infrastructure, software engineering, data engineering, and MLOps practices, with a focus on production-grade AI deployments and scalable

Qualifications

• Hands-on experience with Google Cloud Platform (GCP).
• Experience managing cloud infrastructure using Infrastructure as Code (IaC), preferably Terraform.
• Strong programming skills in Python.
• Experience designing, developing, deploying, and operating production-grade AI solutions.
• Solid understanding of software architecture, distributed systems, and cloud-native app development.
• Experience with MLOps/LLMOps, including CI/CD, version control, automation, monitoring, and observability.
• Experience with containerization and orchestration technologies such as Docker and Kubernetes.

Responsibilities

• Design, develop, deploy, and operate AI/ML solutions across a broad spectrum of use cases.
• Collaborate with Data Scientists, Data Engineers, and business stakeholders to translate requirements into scalable production solutions.
• Ensure AI/ML solutions can be developed, deployed, monitored, and managed within a modern GCP environment.
• Build and evolve tooling and infrastructure to support AI products at scale.
• Translate prototypes, models, and AI concepts into production-ready solutions.

Skills

Python

AI/ML engineering

Distributed systems

MLOps

CI/CD

Cloud-native

Docker

Tools

GCP

Terraform

Kubernetes

Docker

Job description

Location: Stockholm, Sweden

Type: Contract

Start Date: ASAP

Duration: 01/02/2027 + possible extensions

We are seeking a Cloud Infrastructure Engineer / AI/ML Engineer to strengthen our AI and Data Science team. The assignment includes designing, developing, deploying, and operating AI solutions across a broad spectrum of use cases, including traditional machine learning, generative AI, and agent-based systems.

You will work closely with Data Scientists, Data Engineers, and business stakeholders to translate analytical and business requirements into scalable, secure, and maintainable production solutions. The role combines software engineering, data engineering, AI/ML engineering, cloud platform development, and MLOps/LLMOps practices.

A key responsibility is to ensure that AI and ML solutions can be efficiently developed, deployed, monitored, and managed within a modern Google Cloud Platform (GCP) environment. You will also contribute to building and evolving the capabilities, tooling, and infrastructure required to support AI products at scale.

We are looking for a technically strong consultant with proven experience building and operating AI-powered applications and platforms in production environments.

Required Skills:

• Hands-on experience with Google Cloud Platform (GCP).
• Experience managing cloud infrastructure using Infrastructure as Code (IaC), preferably Terraform.
• Strong software engineering background with excellent programming skills in Python.
• Experience designing, developing, deploying, and operating production-grade AI solutions.
• Solid understanding of software architecture, distributed systems, and cloud-native application development.
• Experience with MLOps and/or LLMOps practices, including CI/CD, version control, automation, monitoring, and observability.
• Experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Ability to work closely with Data Scientists and translate prototypes, models, and AI concepts into production-ready solutions.

Bonus Skills:

• Experience with Vertex AI or similar AI/ML platforms.
• Experience building Generative AI applications, RAG solutions, AI assistants, or agent-based systems.
• Experience integrating LLMs with enterprise tools, APIs, data platforms, and business processes.
• Experience developing tools, services, or platforms that enable AI agents to interact with enterprise systems.
• Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Experience working in large-scale enterprise environments with high requirements on security, reliability, and governance.