Fullstack AI Engineer

All engineers should be able to build AI native products across all platforms (web, mobile, desktop) at high velocity and independently.

Below is the minimum bar for engineers on my teams with resources to help them upskill and fill gaps. This bar is the floor, not the ceiling. I don’t expect everyone to be above the floor on day one. All new hires must meet this bar to be eligible for interviews.

Behavioral

  1. Ownership: Take end-to-end responsibility for tasks or projects. Take initiative to solve problems that you see.
  2. Curiosity & Continuous Learning: Stay on top of new AI frameworks and best practices.
  3. Collaborative Problem-Solving: Work openly and effectively with other team members.
  4. High-Velocity Mindset: Optimize for quick iteration, shipping MVPs, and learning.
  5. Show-and-Tell: Talk is cheap, and prototypes are cheaper. Show instead of making proposals.
  6. User obsessed: Design technology that makes people’s experience more natural and seamless. We care about building an exceptional user experience.
  7. Detail-focused: Exceptional outcomes come from exceptional attention to detail to every interaction and every pixel.

Technical

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LanguagesTypescript, SQL, Bash, HTML5, CSS3, Python
Frameworks/RuntimeReact, NextJS, React Query, Zustand, React Native, Expo, TailwindCSS, RadixUI, DrizzleORM, Zod, NodeJS, Bun, WebRTC, PyTorch
ToolingTurborepo, ESLint, Prettier, NPM/PNPM, Git, AI codegen
DatabasesPostgres (incl PGVector, JSON datatype), Object stores
InfrastructureDocker, Cloudflare (Workers, Durable Objects, Pages, DNS, CDN), Vercel, Inngest, Render, Trigger.dev, Datadog, Together.ai, LangSmith, Hugging Face, Replicate, Braintrust
AI EngineeringLarge Language Models, Multi-Modal Models, Image Generation, In-painting, Voice Models
AI Concepts (learn over time)Basic Prompting, Chain of Thought prompting, N-shot prompting, Prompting reasoning models, Tool calling, Preprocessing unstructured data, ReAct, Agent basics, Advanced agentic patterns, Evals, Memory, Generative UI, Streaming, Real-time, Multi-agent systems, Guardrails, Citations, Vector databases, RAG, Text embeddings, Knowledge graphs, Query routing, Synthetic data, Fine-tuning, RLHF, Diffusion Models, MCP, Computer use, Using/Serving Multi-modal OSS models

Note: this list will be updated from time to time.

Resources

  1. www.deeplearning.ai
  2. https://www.udacity.com/course/generative-ai–nd608
  3. https://frontendmasters.com/
  4. https://sdk.vercel.ai/
  5. https://langchain-ai.github.io/langgraphjs/tutorials/quickstart/
  6. https://docs.smith.langchain.com/
  7. AI News
  8. X

Some amazing open-source projects from recent times that show you how to build generative AI products:

  1. https://github.com/midday-ai/midday
  2. https://github.com/vercel-labs/gemini-chatbot
  3. https://github.com/stackblitz/bolt.new
  4. https://github.com/calcom/cal.com

Find me elsewhere on Twitter/X, Farcaster, and Github. I don't use LinkedIn much and emailing the hello@ alias on this domain is the best way to get in touch.