AI Solution Engineer

About the Role The AI Solutions Engineer owns the product side of that build: turning ideas into real AI agents, copilots, and intelligent applications that employees and business teams use every day. You'll work closely with the AI Platform & Integrations Engineer, who builds the underlying data/connectivity layer — your job is to build fast on top of it and get things into people's hands. Key Responsibilities • Rapidly prototype AI agents and copilots using tools like Copilot Studio, Azure AI Foundry, and various LLM APIs (OpenAI, Claude, Gemini) • Build RAG pipelines and MCP-enabled workflows that connect AI systems to real data and tools • Turn stakeholder ideas into working proof-of-concepts fast, then iterate based on feedback • Write production-adjacent Python and API integration code • Evaluate and pressure-test new AI tools and models as they emerge, and make pragmatic build-vs-buy calls • Partner with the Platform & Integrations Engineer to plug prototypes into real enterprise data instead of mock data • Present working demos to non-technical stakeholders and translate feedback into next iterations What We're Looking For • 2–3 years of experience building software, ideally with some hands-on AI/LLM project work (professional or personal) • Strong Python skills and comfort calling and chaining APIs • Hands-on experience with at least one LLM API (OpenAI, Anthropic, Gemini, etc.) — side projects and hackathon builds count • Familiarity with RAG concepts (embeddings, vector search, retrieval pipelines) • Exposure to MCP, Copilot Studio, or Azure AI Foundry is a strong plus — willingness to learn them fast is a requirement • A portfolio, GitHub, or demo-able project that shows you can build, not just talk about building • Comfortable presenting unfinished, imperfect work to get feedback quickly • High tolerance for ambiguity — you'll often get a one-line idea and need to run with it