Hire AI Developersin 2 weeks
BearPlex AI developers ship production AI features end-to-end: frontend integration, backend API design, LLM integration, prompt engineering, evaluation, deployment. Generalist AI builders who handle the full stack of an AI product.
What a AI Developer actually does at BearPlex
An AI developer at BearPlex ships production AI features across the full stack: from the React component that renders the AI feature, through the backend API that handles the LLM integration, through the prompt engineering and evaluation that determines quality, to the deployment and operational infrastructure that keeps it running. They work in TypeScript / Python with modern frontend frameworks (Next.js, React, Vue), backend APIs (FastAPI, Express, NestJS), LLM integrations (Vercel AI SDK, Anthropic SDK, OpenAI SDK), and the production AI tooling stack (LangSmith, Helicone, Promptfoo). They're generalists who can take an AI feature from spec to production deployment without needing handoffs between frontend, backend, and ML specialists. This makes them ideal for fast-moving teams that need to ship AI features quickly without coordinating across multiple engineering disciplines.
Sample engineer profiles
Anonymized to respect engineer privacy. Full bios shared under NDA during scoping.
Built and shipped 14 AI features for a Series C SaaS in 18 months: single full-stack engineer for AI feature work, partnered with PMs and designers.
Shipped a production AI assistant for a fintech startup: full-stack ownership from React UI to Python backend to OpenAI integration to deployment.
Built a customer-facing AI chatbot product solo over 4 months for a B2C startup: full-stack ownership including auth, billing integration, and AI feature engineering.
Lead developer for a production AI platform: shipped 8 customer-facing AI features over 12 months, owns full stack from UX to inference infrastructure.
Skills matrix
The capabilities every BearPlex AI Developer brings on day one.
| Skill | Proficiency | Typical tools |
|---|---|---|
| Modern frontend AI integration | Expert | Next.js · React · Vercel AI SDK · Streaming UX patterns |
| Backend API design for AI features | Expert | FastAPI · Express · NestJS · tRPC |
| LLM integration (Anthropic, OpenAI, Google) | Expert | Anthropic SDK · OpenAI SDK · Vercel AI SDK · LiteLLM |
| Prompt engineering and evaluation | Expert | System prompt design · Promptfoo · LLM-as-judge |
| RAG implementation (basic to intermediate) | Advanced | LlamaIndex · Vercel AI SDK RAG primitives · pgvector |
| Database and persistence layer | Expert | PostgreSQL · Drizzle ORM · Prisma · pgvector |
| Authentication and authorization | Expert | Clerk · Auth0 · NextAuth · SAML / OAuth patterns |
| Deployment and infrastructure (modern PaaS) | Expert | Vercel · Fly.io · Railway · Cloudflare Workers |
| Production observability | Advanced | LangSmith · Helicone · OpenTelemetry · Sentry |
| Cost optimization (prompt caching, model routing) | Advanced | Anthropic prompt caching · OpenAI prompt caching · model routing patterns |
| Real-time and streaming UX | Expert | Server-sent events · WebSockets · Vercel AI SDK streaming |
| AI feature design partnership with PMs / designers | Expert | Spec interpretation · design review participation · iteration partnership |
How we vet AI developers
Technical screen
60-minute deep-dive on past full-stack AI work. We probe: did they ship features end-to-end (frontend through deployment) or only handle one slice? How did they handle prompt engineering vs UI design vs API design as integrated work? We screen out engineers whose 'AI work' was purely backend or purely frontend.
Live build exercise
We give the candidate a realistic AI feature spec (e.g., 'add an AI chat feature to this CRUD app') with starter code and 2 hours. They must integrate the LLM, build the UI, design the API, and demonstrate it working. We're looking for: full-stack judgment, pragmatic shortcuts, eval-aware thinking.
Architecture interview
Whiteboard a complete AI feature for a realistic product scenario: frontend, backend, LLM integration, persistence, observability, cost considerations. We probe for: holistic thinking, pragmatic trade-offs, awareness of common production failure modes.
Reference checks + paid trial
Two engineering reference checks plus a 21-day paid trial on a real client engagement. We don't take engineers off trial until both Hamad and the client engineer report 'I want this person on the team next sprint.'
What clients say
“Their AI developer single-handedly shipped 12 AI features over 8 months. The full-stack ownership eliminated the handoff overhead between frontend, backend, and ML specialists that was killing our previous velocity.”
“Best generalist AI engineer I've worked with. He could ship the React component, the FastAPI endpoint, the prompt design, and the deployment all in the same week. Our internal team learned a lot from his integrated way of working.”
“We needed an AI developer who could move fast without waiting for handoffs. The BearPlex engineer was perfect for that: full-stack capability, pragmatic judgment, didn't over-engineer.”
Hiring AI developers: questions answered
For most growth-stage product roadmaps, yes: a senior AI developer can ship the bulk of typical AI features (chat, RAG, basic agents, AI-augmented workflows). For more complex initiatives (production agent systems, fine-tuning, ML platforms), we pair AI developers with AI engineers who go deeper on those specific domains.
Yes: that's the role. Frontend (React, Next.js, Vue), backend (Python with FastAPI, TypeScript with Node), database (PostgreSQL primarily, sometimes others), LLM integration, deployment. They're generalists who can take features from spec to production without coordination overhead.
Yes: we work with whatever your stack is. Standard modern stacks (Next.js / React, Python / FastAPI, Postgres) are our default; we adapt to other stacks (Ruby on Rails, Django, Java, .NET) when needed.
Highly dependent on scope. A simple AI chat feature with RAG: 2-4 weeks from spec to production. A complex AI-augmented workflow: 6-12 weeks. A complete AI assistant product: 3-6 months. Our developers ship faster than the industry average because of full-stack capability: no handoff delays.
Primarily Lahore, Pakistan (HQ) with client-facing presence in Austin and Doha. Time zone overlap with US clients is 5-9 hours; we structure engagements with daily 2-3 hour overlap windows for synchronous work, async handoff for the rest.
Embedded developer engagement: $20K-$35K monthly retainer (typically 3-12 months). Per-feature engagement: $30K-$120K depending on feature complexity. Most successful engagements are embedded for 6-18 months with the developer as part of the client's product engineering team.
Yes: common engagement pattern. Our AI developers participate in product spec discussions, design reviews, and customer feedback sessions. They're not pure engineers who take Jira tickets: they participate in the full product process.
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