LLM Solutions Architect

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.

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.