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About the role
Description What You'll Do : Problem / Opportunity Discovery Sit with a business or clinical leader and reframe an idea or problem into something concrete and buildable. Know what to build by the end of the conversation; have a working prototype to react to by the end of the week. Partner closely with product, design, and client stakeholders to translate ambiguous ideas into software that ships. Demo live without a slide deck. Reframe problems out loud. Don't get stuck waiting for someone else to make the decision. Lead POCs, innovation sprints, and internal research experiments to validate emerging AI techniques. Build (rapid) with AI When the brief is explicit, head down and produce : Build modular backends in Python or TypeScript aligned with clean architecture, OOP, SOLID, and domain-driven design. Create full stack applications, APIs, agents, workflows, and similar systems using frameworks such as Next.js, React, Quick API, Fastify, FastMCP, and Hono. Architect and ship production-grade agentic applications using Lang Graph, AutoGen, Claude Agent SDK, OpenAI Assistants, or your own orchestration layer. Integrate frontier and self-hosted LLMs (Claude, GPT, Gemini, open-weight models) with tools, data, and external systems through MCP and custom connectors. Apply RAG techniques where they actually help : vector databases (Pinecone, Chroma, Weaviate, pgvector), hybrid retrieval with Elasticsearch or Solr, and BM25 + similarity search. Work across relational, document, key-value, and graph stores as the problem demands; use event-driven patterns where they fit, not by default. Design prompt and context engineering frameworks that optimize accuracy, repeatability, cost, and latency. Use AI-assisted development tools (Claude Code, GitHub Copilot, Cursor, Codex) through structured workflows, native instructions, templates, and sub-agents with discipline and review. Fine-tune or adapt models where the problem genuinely calls for it. Test .
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