The Difference Between "AI-Aware" and AI-Native
AppGenie engineers are different. Before they join our network, they're assessed on actual AI-tool proficiency — not just listed skills.
Deep Engineering Fundamentals First — AI is a multiplier, not a replacement. Every AppGenie engineer starts as a strong software developer — solid on architecture, data structures, system design, and clean code. AI makes them faster. It doesn't replace the skill.
AI Tools Embedded in Their Daily Workflow — GitHub Copilot, Claude, Cursor, v0, LangChain, LlamaIndex — our engineers use these daily. When you brief them on a task, they're already thinking about how AI can accelerate it.
Product Thinking, Not Just Ticket Execution — Our engineers work with founders and PMs directly. They flag scope issues, suggest better implementations, and push back when a technical decision doesn't serve the product. They're not ticket-closers.
Full Technical Coverage. One Talent Pool.
Whatever your stack, we have engineers who've shipped in it.
Core Engineering
- Frontend: React, Next.js, Vue, Angular, TypeScript, Tailwind
- Backend: Node.js, Python (FastAPI / Django), Go, Java, .NET, PHP (Laravel), Ruby on Rails
- Mobile: React Native, Flutter, Swift, Kotlin
- Cloud & Infrastructure: AWS, GCP, Azure, Docker, Kubernetes, Terraform, CI/CD, observability
AI & Automation
- LLM Integration: OpenAI, Anthropic Claude, open-source models, RAG pipelines, prompt engineering
- Agentic Systems: AI agents, n8n, Zapier, Make, internal API orchestration
- ML & Data: Python, scikit-learn, PyTorch, model training and evaluation, Airflow, data pipelines
- AI-Accelerated Dev: Test generation, automated code review, documentation automation, debugging assistants
Data & Product
- Data Engineering: ETL pipelines, SQL/NoSQL, data warehousing, dbt, Spark
- Analytics: Event tracking, dashboards, A/B testing infrastructure, Mixpanel, Amplitude
- Product Sense: Scoping features from business requirements, surfacing trade-offs, contributing to roadmap discussions
Not a buzzword. A measurable change in output velocity.
Our engineers use AI to write, refactor, and test code faster — not as a shortcut, but as a force multiplier on solid fundamentals.
• They generate test coverage in minutes, not hours — using AI to write edge-case tests against their own implementations.
• They automate repetitive engineering tasks: deployment scripts, QA checks, changelog generation, documentation.
• They follow rigorous security and privacy practices when using AI — no customer data going to third-party LLMs without explicit approval.
• They stay current — through our internal AI engineering playbooks, shared prompts, and weekly knowledge sessions.
The result: an engineer who ships 30–40% faster than a traditional hire, without taking shortcuts on quality, security, or maintainability.
Frequently Asked Questions
It means we test it — not just ask about it. Candidates complete practical tasks that require AI-tool proficiency: building a RAG pipeline, using Cursor to refactor legacy code, writing a prompt that generates reliable structured output. Listing a skill doesn't pass the test. Using it does.
No. The majority of our placements are for classic product engineering: web apps, mobile apps, APIs, internal tools, data infrastructure. AI proficiency makes them faster and more capable across all of it — it's not a specialisation, it's a baseline.
Yes. We match engineers based on your stack and roadmap. Most are ready to open a PR within their first week — not their first month.
Based in Vietnam (GMT+7), our engineers provide 6 hours of daily overlap with US Eastern Time. For EU clients, we have near-full overlap. You choose the schedule that fits your team.
Brief us today — you'll have a shortlist in 48 hours and a signed engineer in 2 weeks.