DevOps & AI Engineer
- Compensation
- Not disclosed
- Location
- Stockholms län, Sweden
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Contract
- Experience
- Not specified
- Category
- Ai engineering
- Posted
Skills
Must have
- Version Control & CI/CD Mastery: Deep expertise in both Perforce and Git, with a strong understanding of their distinct workflows and underlying models, as well as hands‑on experience designing and au
- Game Engine & Asset Pipeline Knowledge: Working knowledge of Unreal Engine 5 project structures and asset pipelines, including a strong understanding of the challenges associated with merging game‑spe
- Applied AI/ML Practitioner: Proven ability to build practical applications using LLMs through APIs, including prompt engineering, context engineering, agentic frameworks, and retrieval‑augmented gener
- Polyglot Developer & Systems Integrator: Proficient in Python, C# for engine tooling, and C++, with full‑stack web development experience (e.g., React and Node.js) for internal dashboard development.
- Autonomous & Strategic Thinker: A highly self‑directed professional capable of independently driving complex projects, strategically leveraging AI orchestration to delegate work to specialized agent s
- Game Industry Experience: Demonstrated experience in the game industry or engine development, with a strong appreciation for the unique technical and workflow complexities of game production.
Full description
We are seeking a highly autonomous, high-output DevOps & AI Engineer to design and build a pioneering AI-assisted system. This solo-developer initiative will automate the complex integration, merging, and migration of game project branches across versions, encompassing both Unreal Engine 5 and our proprietary APEX engine projects.
You will be responsible for creating an intelligent system that not only manages Git and Perforce workflows but also integrates seamlessly with Jenkins for robust preflight validation and automated testing.
Leveraging advanced LLM-powered agentic workflows, the system will classify, triage, and reliably resolve merge conflicts, moving beyond blind binary merges to intelligently interact with engine APIs and scripting environments.
This is a critical role for someone ready to independently build a sophisticated backend agent framework and an intuitive frontend dashboard while leveraging an existing, mature CI/CD infrastructure.
Note: Research shows under-represented groups in the industry might not apply to a job if they don’t check off every requirement. At Avalanche, we’re more about your drive to learn and interest in tackling challenges than what’s on your resume! So don’t stress if you don’t tick every box— shoot us your application anyway! Diverse opinions and different experiences are what make our teams so great, not itemized lists of qualifications.
What you’ll do
- Design and Build an AI-Powered Integration System: Architect, develop, and deploy a comprehensive AI-assisted platform that automates game project branch integration across Unreal Engine 5 and APEX. The system will support both Git and Perforce version control and integrate seamlessly with Jenkins-based CI/CD workflows.
- Develop Intelligent Conflict Resolution: Implement advanced LLM-powered agentic workflows to classify, triage, and resolve merge conflicts. Focus on deep contextual understanding, multi-pass investigation, and intelligent decision-making rather than relying on blind binary merges.
- Engine and Scripting Automation: Leverage native Python scripting and engine APIs to interact directly with Unreal Engine, automate tasks such as Blueprint recompilation and contract-change updates, and extend these capabilities to proprietary engines such as APEX.
- Full-Stack Development and Workflow Orchestration: Independently build both the backend agent framework and an internal, project-specific frontend dashboard. Design and manage complex state machines for "Paused Integration" workflows, enabling human-in-the-loop intervention and continuous learning through self-reflection mechanisms based on human resolutions.
- Define and Track Success Metrics: Establish, monitor, and refine key performance indicators (KPIs) that measure AI effectiveness, including Attempt Rate, Escalation Rate, and Rollback Rate, ensuring continuous improvement, operational efficiency, and system reliability.