Senior AI Enablement Engineer hos LeoVegas Group i Stockholm

Your Mission

Accelerate how the entire development organization works by finding high-impact use cases for AI, building the solutions that automate them, and spreading those wins so they scale across teams.

You will be responsible for:

Use Case Discovery: work directly with engineering and product teams to map their workflows, spot repetitive or slow processes, and identify the highest-impact opportunities for AI-driven automation across the development organization.

Rapid Prototyping & Delivery: act as a builder - lead the technical design and implementation of working solutions such as prompt workflows, lightweight agents, scripts, and integrations that prove value fast, then partnering with the AI Efficiency Architects to harden anything that needs to run in production.

Reusable Enablement Assets: create templates, playbooks, and repeatable patterns so a proven AI win can spread from one team to many without bespoke work each time.

Adoption & Measurement: drive genuine adoption of AI tooling across teams and tie every solution back to concrete outcomes - time saved, throughput, and delivery speed - so impact is visible and quantifiable to leadership.

Feedback to the Platform: surface recurring needs and friction back to the AI Efficiency Architects, so the internal AI gateway and tooling evolve toward what teams actually need.

Technical Mentorship & Influence: mentor senior engineers on AI best practices and influence engineering roadmaps.

Our successful candidate will have the following:

• ESSENTIAL SKILLS

Staff-Level Engineering Foundations: 8+ years of professional software engineering experience (Java, Python, Go, or TypeScript) with a proven track record of delivering complex, distributed systems in a production environment.

Production-Ready AI Expertise: Deep, hands-on experience moving AI from a cool prototype to a real-world tool. You don't just write basic prompts - you know how to build reliable agents and RAG workflows, and you understand the practical side of LLMs, like managing costs, speed, and context limits to make tools actually work for production.

Strategic Problem-Solving: You have a strong bias for action and can take a vague problem and turn it into a working solution fast. While you focus on delivering value quickly rather than chasing perfection, you always think big - building smart, reusable tools that fix root problems for hundreds of developers, not just one team.

Collaboration & Communication: excellent interpersonal skills and the ability to work alongside many different teams, understand their needs, and bring them along on the adoption journey.

SDLC/DevOps Familiarity: working knowledge of the tools that underpin the software delivery lifecycle (e.g., GitLab/GitHub, CI/CD pipelines, source control) so that solutions fit naturally into how teams already work.

Self-Direction: capacity to manage a varied portfolio of work across multiple teams, prioritize for impact, and deliver with minimal supervision.