Be part of shaping how AI transforms how we work.
You’ll scout emerging AI technologies, pilot new tools, and coordinate cross-departmental adoption projects.
A key focus will be managing and enhancing our internal Knowledge Hub — ensuring information flows seamlessly and employees can leverage AI to its fullest.
What you will do : Design and implement agentic architectures — multi-step planners, ReAct loops, tool-use orchestration, and memory systems Build reliable agent pipelines that handle ambiguity, recover from failures, and know when to escalate to humans Integrate LLMs (Claude, GPT, open-source models) with external tools, APIs, databases, and enterprise systems Develop evaluation frameworks to measure agent reliability, accuracy, cost, and latency in production Implement guardrails, safety layers, and human-in-the-loop controls for autonomous systems Optimize for real-world constraints: token costs, latency budgets, rate limits, and context window management Collaborate with product and engineering teams to identify high-value automation opportunities across the digital entertainment portfolio Contribute to internal tooling and frameworks that accelerate agent development across the organization Opportunity to work for a dynamic international company with a flat hierarchical structure, where your voice matters and your impact is seen.
5+ years of software engineering experience, with at least 1-2 years focused on LLM-based systems  Hands-on experience building agentic systems — not just prompt engineering, but orchestration, tool use, and state management  Strong proficiency in Python; familiarity with TypeScript is a plus  Deep understanding of LLM APIs (Claude, OpenAI, etc.
), including function calling, structured outputs, and streaming  Experience with at least one agent framework (LangGraph, CrewAI, AutoGen, custom frameworks) Solid software engineering fundamentals: testing, CI/CD, observability, version control Experience with RAG pipelines, vector databases (Pinecone, Weaviate, pgvector), and retrieval systems Experience with MCP (Model Context Protocol) or similar tool-integration standards