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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # LLM Solutions Architect - **Company:** Xsolla (USA), Inc. - **Location:** United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Video Game Development, Python (Programming Language), Open Source Technology, Software Product Management, Search Technologies, Software Deployment, Large Language Models, Multi-Agent Systems, Kubernetes, Deployment Automation - **Published:** September 4, 2026 - **Apply:** https://startup.jobs/llm-solutions-architect-xsolla-7964207 ## About the Role We are looking for an LLM Solutions Architect who is a builder at heart - someone who shapes strategy and ships real systems - to join our Monetization Products team. The best candidate will be someone who thrives in a fast-paced, highly collaborative, and exceptionally dynamic setting and is excited to drive AI product hypotheses from prototype to production-grade engineering. Strong technical architecture skills are essential, along with experience in designing and deploying LLM-powered systems in production. The ability to influence product direction, prototype rapidly, and communicate trade-offs clearly to both engineers and executives will be key to your success in this role. If you're passionate about advancing AI technology solutions and love building intelligent, agent-first capabilities that transform how game developers monetize their products, we would love to hear from you!, * 5+ years of engineering experience, with at least 2 years designing and deploying LLM-powered systems in production. * Proven track record designing agentic systems: tool-use, function calling, multi-step reasoning, orchestration, and error recovery at production scale. * Experience designing AI systems for engineering team ownership - including observability standards, handoff documentation, and runbooks that let other teams maintain what you build. * Hands-on experience with major LLM APIs (OpenAI, Anthropic, Google Gemini) and at least one open-source model stack. * Experience building RAG pipelines with vector databases and orchestration frameworks (LangChain, LlamaIndex, or custom). * Strong Python engineering skills - production-grade LLM services, not just notebooks. * Demonstrated ability to influence product direction: you have shaped what gets built, not just how. * Clear communication in both directions: architectural trade-offs to engineers, business outcomes to executives. Nice to Have * Background in gaming, payments, or e-commerce - understanding of developer workflows, monetization models, or merchant operations. * Fine-tuning experience (PEFT/LoRA) for domain-specific model adaptation. * Experience with multi-agent orchestration frameworks (AutoGen, CrewAI, or custom). * Familiarity with LLM evaluation frameworks (RAGAS, DeepEval, or custom harnesses). * Exposure to EU AI Act, GDPR, or other AI compliance frameworks. ## Description * Design end-to-end agentic architectures - tool-use schemas, intent parsing, multi-step orchestration, and safety guardrails - engineered for long-term ownership by product engineering teams, not solo maintenance. * Define the multi-modal interface strategy across our product portfolio: how the same capability is exposed via UI, API, SDK, and agentic natural language - consistently and without duplication. * Design the horizontal LLM platform layer - shared RAG pipelines, prompt libraries, vector search infrastructure, and evaluation frameworks - that product engineering teams can build on and operate independently. * Prototype rapidly to validate AI product hypotheses before full engineering investment. Prototype acceptance by product teams is a primary success signal. * Ensure every system you architect comes with the observability, documentation, and engineering runbooks needed for a product squad to take ownership confidently. * Shape product strategy alongside Product leadership: actively influence what AI capabilities get prioritized, in what order, and with what trade-offs. * Select and govern LLM providers and deployment strategies per use case - balancing cost, latency, accuracy, and privacy requirements. * Drive alignment across Engineering, Product, and Design on what 'agent-ready' means for each product surface. * Mentor engineers on LLM integration patterns, agent evaluation, and production deployment practices - building the team's capability to own what you design. ## Related Videos - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [Building an agentic software factory: How we rebuilt product development at Pipedrive](https://www.wearedevelopers.com/videos/100261-building-an-agentic-software-factory-how-we-rebuilt-product-development-at-pipedrive) - [100 times more frequent deployments: How did we create a high performance team?](https://www.wearedevelopers.com/videos/1069-100-times-more-frequent-deployments-how-did-we-create-a-high-performance-team) - [How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge](https://www.wearedevelopers.com/videos/1328-how-to-avoid-llm-pitfalls-mete-atamel-and-guillaume-laforge) - [Instant KAI Sandboxes with vCluster: Multi-Tenant, Multi-Scheduler GPU Sharing](https://www.wearedevelopers.com/videos/100333-instant-kai-sandboxes-with-vcluster-multi-tenant-multi-scheduler-gpu-sharing) - [LLMs in the wild: Building an AI agent that survives production](https://www.wearedevelopers.com/videos/100319-llms-in-the-wild-building-an-ai-agent-that-survives-production) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Who Owns Your Content in the Age of LLMs?](https://www.wearedevelopers.com/magazine/610-who-owns-your-content-in-the-age-of-llms) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)