Lead FullStack Engineer
AI full-stack tech lead in Abu Dhabi who owns the technical direction of a product engineering team: architecting Node.js backend services, shipping React/Next.js frontend features, and integrating LLMs, RAG pipelines, and automation, while leading backend, frontend, and AI engineers and championing AI coding tools like Copilot and Cursor.
- Compensation
- Not disclosed
- Location
- Abu Dhabi, United Arab Emirates
- Work setup
- Not specified
- Seniority
- Lead
- Employment
- Not specified
- Experience
- 6+ years
- Category
- Fullstack
- Posted
Skills
Must have
- 6-10 years of experience
- Node.js — 6+ years, expert level
- TypeScript — strong production experience
- REST API & GraphQL design
- PostgreSQL / MySQL — schema design & tuning
- AWS / GCP / Azure cloud-native services
- CI/CD — GitHub Actions, pipelines, IaC
- AI coding tools — Copilot, Cursor, or similar
- System design & architectural decision-making
- Agile / iterative product delivery
- Cross-functional team leadership (5+ engineers)
- LLM API integration (OpenAI / Anthropic)
- Docker & Kubernetes
- MongoDB / Redis / caching strategies
- Microservices & distributed systems
- React / Next.js — full stack delivery
- Python — proficient (FastAPI, Django, scripts)
Nice to have
- AI/ML frameworks: LangChain, LlamaIndex, Hugging Face, open-source LLMs (Mistral, LLaMA)
- Vector databases: Pinecone, Weaviate, Qdrant, or pgvector for RAG pipelines
- Data engineering: ETL pipelines, data streaming (Kafka), or time-series systems
- DevOps depth: Terraform, Helm, advanced Kubernetes, observability platforms (Datadog, Grafana)
- Experience working on AI-native or automation-first products
- Active open-source contributions or visible technical portfolio
Full description
We are looking for a seasoned AI Full Stack Tech Lead to own the technical direction of our product engineering team.
Team Scope: Backend · Frontend · AI/ML · DevOpsExperience Level: 6–10 YearsKey Stack : Node.js (primary) · Python · TypeScript · React/Next.js
About the Role
We are looking for a seasoned AI Full Stack Tech Lead to own the technical direction of our product engineering team. Inspired by profiles like senior engineers with a track record across backend services, frontend delivery, and AI product integration — this role demands both depth and breadth. You will architect and build backend systems in Node.js, ship full-stack features using React/Next.js, integrate AI capabilities (LLMs, automation, data pipelines), and lead a cross-functional team of backend, frontend, and AI engineers. You bring a strong AI-first mindset and actively leverage AI coding tools to elevate team velocity.
What makes this role unique-
You are not just a backend engineer — you lead end-to-end: API, UI, and AI-AI is core to the product, not a side feature — LLM integration is day-one work-You actively use AI coding assistants (Copilot, Cursor, Claude Code) and champion them across the team-You will shape architecture decisions that directly affect product performance and customer experience
Key Responsibilities
Backend Architecture & Engineering
- Design and own scalable backend systems using Node.js — REST APIs, microservices, event-driven architecture
- Build Python-based services for data pipelines, ML integration, and automation workflows
- Architect database schemas and storage strategies across SQL (PostgreSQL) and NoSQL (MongoDB, Redis)
- Own observability: structured logging, monitoring, alerting, and performance profiling in production
- Drive CI/CD pipelines, infrastructure-as-code, and DevOps best practices across the team
Full Stack Development
- Build and review frontend features using React / Next.js and TypeScript
- Define frontend architecture standards: component design, state management, API integration patterns
- Collaborate with designers and product managers to deliver polished, performant user-facing features
- Ensure frontend quality through code reviews, performance audits, and cross-browser compatibility
AI & ML Integration
- Integrate LLM APIs (OpenAI, Anthropic, open-source models) into product features
- Design and implement RAG pipelines, prompt engineering workflows, and AI-powered automation
- Work with the AI research team on model serving, embedding pipelines, and vector search (e.g. pgvector, Pinecone)