Hire Remote AI Product Engineers

Hire AI Product Engineers Who Ship AI Products Users Love

There’s a gap between AI researchers who build models and software engineers who ship products — and AI product engineers live in that gap. They combine LLM/ML expertise with full-stack engineering skills to take AI from prototype to production user experience.

We match you with senior AI product engineers who’ve launched user-facing AI products for fast-growing startups and Fortune 500 enterprises: chatbots with real retention, AI writing tools with measurable engagement, and AI-augmented workflows that users adopt. Engineers who can own the full product surface — the AI model integration, the API layer, the frontend experience, and the evaluation framework.

Start in days, not months. Pay 50% less than equivalent US-based AI talent.

What Our AI Product Engineers Build

AI-Native Application Development

Full-stack AI applications with React/Next.js frontends, Node.js or Python backends, and LLM/ML integration — shipped as production SaaS products with real users, not demos.

AI Feature Integration

Adding AI capabilities to existing products: AI search, smart recommendations, auto-complete, AI assistants, and intelligent automation — integrated into existing product architectures with minimal disruption.

Conversational AI Products

Production chatbots and AI assistants with memory, context management, multi-turn dialogue, and fallback handling. Built for user retention, not just demo impressiveness.

AI-Augmented Workflows

AI-powered workflow tools that augment human work: AI writing assistants, AI-powered code review, intelligent data entry, and automated report generation — with human-in-the-loop design that increases adoption.

AI Evaluation & Product Analytics

Evaluation frameworks that measure AI product quality in terms of user outcomes — task completion rates, user satisfaction, AI accuracy on real queries. Connected to product analytics and A/B testing infrastructure.

AI Product Engineering Stack

Frontend: React, Next.js, TypeScript, Tailwind CSS, Vercel AI SDK

Backend: Node.js, Python (FastAPI/Django), GraphQL, REST APIs

AI/ML: OpenAI, Anthropic, LangChain, LlamaIndex, Hugging Face, Pinecone

Infrastructure: Vercel, AWS, GCP, Docker, Kubernetes, GitHub Actions

Analytics: Mixpanel, Amplitude, PostHog, LangSmith, custom evaluation pipelines

Databases: PostgreSQL, MongoDB, Redis, Supabase, PlanetScale

Client Success Story: AI Writing Tool — $8M ARR in 14 Months

A Series A SaaS startup building an AI writing tool for marketing teams needed engineers who could ship fast and iterate on product quality simultaneously. Our AI product engineers built a Next.js + FastAPI platform with GPT-4o integration, brand voice customization, an in-editor revision workflow, and an output quality scoring system using a custom evaluator. Monthly retention improved from 34% to 67% after the quality evaluation system helped identify and fix the prompts causing low-quality outputs. The product grew to $8M ARR within 14 months of launch.

Client Success Story: Enterprise AI Search — 44% Reduction in Support Escalations

A B2B software company wanted to replace its keyword-based documentation search with semantic AI search that could answer natural language questions. Our AI product engineers built a RAG-powered search interface using Next.js, Pinecone, and a fine-tuned embedding model — integrated into the existing product without a full redesign. The AI search answered 62% of queries directly without requiring the user to read full documentation. Support escalations dropped 44%. NPS for the documentation experience improved from 12 to 48.

Why Companies Choose Our AI Product Engineers

• Full-stack AI fluency: They ship the frontend, backend, and AI integration — no coordination overhead between specialists for straightforward AI product work
• Product instincts: They think about user adoption and retention, not just model accuracy
• Evaluation-driven: They build measurement systems that tell you when the AI product is getting better or worse
• 50% cost savings: Senior AI product engineering at a fraction of US market rates
• Fast start: Most engagements begin within 1–2 weeks

Engagement Models

• Individual AI Product Engineer — One senior AI product engineer who can own an AI feature or product end-to-end.
• AI Product Pods (2–3 engineers) — AI product engineer paired with a UX engineer and backend specialist. Common for teams launching new AI-native products on tight timelines.
• Full AI Product Teams (4–10+ engineers) — Complete squads for AI-native product companies scaling their engineering organization.
• Contract-to-Hire — Evaluate real shipped output before committing long-term.

How To Vet AI Product Engineers

Our vetting identifies engineers who can ship full-stack AI products — not just use AI APIs in isolation.

1. Technical screening — Full-stack fundamentals (APIs, databases, frontend), LLM integration patterns, RAG architecture basics, and AI evaluation frameworks. Over 90% of applicants do not pass this stage.
2. Portfolio review — Working demos or production URLs of AI products they’ve shipped. We look for evidence of real users, measurable outcomes, and iterative product thinking.
3. Live product challenge — Build a minimal AI product feature from scratch in a time-boxed session. Evaluated on product decision-making, code quality, and AI integration approach.
4. Communication screening — AI product engineers must translate between business goals, user needs, and AI capabilities. We assess this explicitly.

What to Look for When Hiring AI Product Engineers

Strong AI product engineers ship products users love — they optimize for user outcomes, not model benchmarks.

What strong candidates demonstrate:

• They can point to shipped AI products with real users and discuss actual retention and engagement metrics
• They understand the product design challenges specific to AI: managing user expectations, handling errors gracefully, and building trust through transparency
• They’ve iterated on AI quality based on user feedback — they know how to run AI-specific A/B tests and evaluations
• They have full-stack fluency — frontend through backend through AI integration — without being a specialist in any one layer

Red flags to watch for:

• Has only built AI demos or prototypes — no experience shipping to production users at scale
• Thinks about AI purely in terms of model accuracy without considering user experience and adoption
• No experience instrumenting and measuring AI product quality with real users
• Requires separate frontend and backend engineers to ship anything — can’t work full-stack

Interview questions that reveal real depth:

• “Walk me through an AI product you shipped. What were the retention metrics? How did you iterate on quality?”
• “How do you design AI error states and failure modes so users don’t lose trust in the product?”
• “What evaluation framework would you build to know if an AI writing assistant is actually helping users produce better content?”

Related Services

• AI Engineers & ML Engineers — Deeper ML/AI engineering for teams building models, not just integrating them.
• Prompt Engineers & LLM Specialists — Specialists in LLM application architecture for teams with existing engineering capability.
• Full-Stack Developers — Generalist full-stack engineers for teams building products without a heavy AI component.
• Front-End Developers — Frontend specialists for AI products that need a dedicated UX engineering focus.

Want to Hire Remote AI Product Engineers?

We source, vet, and place senior AI product engineers who’ve shipped user-facing AI products — engineers who combine LLM integration expertise with full-stack engineering skills and the product instincts to build AI features users actually adopt. Whether you need one AI product engineer or a complete AI product team, we make it fast, affordable, and low-risk.

Get matched with AI product engineers →

Ready to hire AI product engineers who’ve shipped real AI products? Contact us today and we’ll introduce you to senior engineers within 48 hours.