FDE AI Native Builder

Job Description:

Responsibilities

• 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 (fast) with AI
• When the brief is clear, 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, Fast 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, Deploy, Productionize
• Spin up the infra, write the evals, wire up the MCP servers, deploy the agents, and harden the bits that survive contact with real users.
• Deploy on AWS, Azure, Cloudflare, or Vercel using containerization (Docker, Kubernetes) or serverless—chosen for fit, not preference.
• Treat evals as a first-class discipline: hands-on harnesses, not theoretical frameworks. Build with a clear-eyed view of where current AI tooling helps and where it falls short.
• Apply engineering practices that hold up in production: TDD, secrets management and rotation, SAST/DAST, structured logging, metrics, tracing, and automated CI/CD (GitHub Actions, Jenkins).
• Own what you build end-to-end, including the infrastructure and operations that keep it running.
• Mentor others on system design, agentic patterns, and AI engineering best practices.

Qualification & Experience    

• 3+ years relevant experience building production applications using AI / agentic development approaches—full stack applications, agents, workflows, MCPs, and more.
• Hands-on experience with agents, not just prompted models. You have wired tools to a model and let it run multi-step using Lang Graph, AutoGen, Claude Agent SDK, OpenAI Assistants, or your own orchestration.
• Active, structured use of AI-assisted development tools (Claude Code, Cursor, GitHub Copilot) with demonstrable workflows, sub-agents, skills, and innovative approaches.
• Strong Python or TypeScript, with OOP, SOLID, 12-factor application development, and microservice architecture. You've built Next.js applications, Fast API services, and similar.
• End-to-end implementation experience with vector databases, retrieval pipelines, and eval harnesses.
• Cloud-native deployment experience across at least one of AWS, Azure, Cloudflare, or Vercel—with Docker, Kubernetes, and GitHub Actions.
• A no-compromise attitude on clean code, TDD, security, observability, scalability, performance, and cost.
• A deep working understanding of how LLMs behave—and where they break—and how to optimize accuracy, latency, and cost.
• Clear writing and a willingness to reframe problems in conversation rather than wait for someone else to define them.
• A real, recent trail of built things: GitHub, a portfolio, side projects, indie tools, or OSS contributions.
• A founder's mindset and genuine appetite for ambiguous, high-impact technical challenges.
• Bachelor's or Master's in Computer Science, Machine Learning, or a related technical discipline.