Description
PLAY, GROW and WIN To be a part of Virtuos means to be a creator. At Virtuos, we harness the latest technologies to make games better and more immersive than ever before. That is why we pride ourselves in constantly pushing the boundaries of possibility since our founding in 2004. Virtuosi is a team of experts – people who have come together to share their mutual passion for making and playing games. People with the same enthusiasm for exploring new ideas and the constant drive to excel in their field. People who believe in earning success through dedication. At Virtuos, we are at the forefront of gaming, creating exciting new experiences daily. Join us to Play, Grow and Win – together. ABOUT THE POSITION We are looking for a Senior AI Engineer to help design and deliver agentic AI systems that power R&D tooling for video game asset pipelines and production workflows. You will help shape the technical direction of our internal agent platform and drive engineering practices around agent loops, memory, evaluation, and safe deployment of LLM-driven applications. This is a senior, hands-on individual contributor role: you will write code, help design the agentic architecture, and partner with stakeholders across studios to turn emerging AI capabilities into production-grade tools. At Virtuos, we harness the latest technologies to make games better and more immersive than ever before. That is why we pride ourselves in constantly pushing the boundaries of possibility since our founding in 2004. Responsibilities Agent platform - Design and build key parts of our internal agent libraries - the core abstractions and developer ergonomics that let teams across the company build agents quickly and consistently. - Help shape the architecture of our central agent runtime - the runtime, registry, and observability surface where agents are deployed, monitored, and governed. - Help define and evolve the agent loop / harness: prompt orchestration, tool invocation, sub-agent delegation, and recovery behavior. - Bring in reference patterns from the broader ecosystem (e.g. open-source agent loops and harness projects) and adapt them to our use cases. Agent loop & harness engineering - Drive prompting strategy at scale: system prompt design, guardrails, mitigation of context poisoning and pollution, and management of hyperparameters (context window sizing, lost-in-the-middle effects, temperature, top-k). - Design tool interfaces for agents: MCP servers, structured inputs/outputs for context, and sub-agent composition patterns. - Advocate for best practices for typed-agent frameworks, with first-class observability and telemetry baked into every agent. - Evaluate and integrate local LLM options where latency, cost, or data-residency requirements demand it. Agent memory - Design and build the key parts of the memory layer used across our agents: conversation history management, context chaining, and episodic memory. - Help define the boundary between short-term working context and long-term persistent memory, including decay/retention policies. - Apply RBAC and tenant isolation to memo