AI Solutions Engineer

Strategic problem-solving

• Translate messy business needs into clear technical solutions
• Understand how sales, partnerships, and ops actually work and then build for those realities
• Decide what to build, automate (N8N), integrate (APIs), or ignore
• Own outcomes, not just deliverables

Rapid prototyping & iteration

• Build integrated, functional MVPs for internal testing using AI coding tools (Claude Code, Cursor, Copilot)
• Design scalable, lightweight database schemas
• Create FastAPI backends and N8N workflows that solve real problems today
• Validate solutions with real users before handoff to production

Technical architecture

• Maintain postgres/Supabase databases with clean schema design and strong data integrity
• Implement semantic search, vector databases, and LLM integrations where they truly add value
• Know when to use Postgres, vectors, or even spreadsheets

Do you have experience in Python?, Do you have a Master's degree?, You are pragmatic, product-minded, and technically sharp. You have built things people actually use and you understand that the best code is often the code you don't write. When you do write it, you ship clean, maintainable, and effective solutions. You are comfortable with ambiguity because you know how to turn vague ideas into actionable next steps., * You understand how companies really operate: sales pipelines, partner relationships, data flows

• You define what to build by understanding why it matters
• 1+ years working with databases (PostgreSQL/Supabase), Python, or shipping production code
• AI-native workflow: Daily user of Claude Code, Cursor, Copilot, or similar tools; you build with AI, not around it
• Pragmatic shipping: Proven track record of building things that work, fast, * Experience with FastAPI, Flask, or similar frameworks
• Familiarity with N8N or other workflow automation tools
• Understanding of vector databases, semantic search, or embeddings
• Experience with LangChain, API design, or LLM integration patterns
• Judgment on when not to build custom solutions

The litmus test: If you have ever been frustrated watching teams build the wrong thing efficiently, or seen a manual process that obviously needed automating, and actually fixed it, we should talk.

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