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 (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.

What You Bring :

- 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.

Bonus Skills / Experience :

- Public writing, talks, or threads about building with AI.

- MLOps and model serving experience (BentoML, MLflow, Vertex AI, SageMaker).

- Streaming and batch ingestion pipelines (Spark, Airflow, Beam, Glue).

- Healthcare or life sciences domain exposure.

- AWS Professional certification or other relevant industry certifications