[Hiring] LLM Solutions Architect @Xsolla

Role Description

You will join Xsolla's Monetization Products team as the person who sits between product strategy and engineering architecture — with strong influence on both. This is not an ML research role. It is not a product management role. It is the bridge: someone who prototypes fast, gets ideas accepted, and designs systems that engineering teams can own and operate at scale.

You will architect our Agents-first approach across the product portfolio — defining how natural language, agentic control, and multi-modal interfaces layer on top of Xsolla's existing product capabilities. Critically, your designs will be built for engineering team ownership from day one: with clear handoff documentation, observable instrumentation, and runbooks that allow product engineering squads to maintain and evolve what you ship — without requiring your continued involvement in day-to-day operations.

What You'll Do

• 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.

Qualifications

• 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.

Requirements

• 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.

Benefits

• 100% company-paid medical, dental, and vision plans.
• Unlimited Flexible Time Off.
• A personalized career roadmap for each employee.
• Investment in professional development through training and educational opportunities.