Lead FullStack Engineer
Summary
Lead AI-driven full-stack development for a product company, owning backend (Node.js, Python), frontend (React/Next.js), and AI integrations (LLMs, RAG pipelines). Architect scalable systems, mentor engineers, and champion AI coding tools.
Experience Level: 6–10 Years
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
- 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
- Collaborate with designers and product managers to deliver polished, performant user‑facing features
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)
- Own reliability and cost tradeoffs for AI features in production — latency, token budgets, fallback strategies
Lead a cross‑functional team of backend, frontend, and AI engineers — day‑to‑day technical direction
Conduct regular code reviews, architectural reviews, and technical design sessions
Mentor engineers at all levels; define team engineering standards and growth paths
Own the technical roadmap and translate product requirements into clear engineering plans
Champion AI coding tools across the team — establish best practices for AI‑assisted development
Required Skills & Experience
- Node.js — 6+ years, expert level
- CI/CD — GitHub Actions, pipelines, IaC
- AI coding tools — Copilot, Cursor, or similar
- Cross‑functional team leadership (5+ engineers)
- MongoDB / Redis / caching strategies
Nice to Have
- 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, or observability platforms (Datadog, Grafana)
- Product sense: Experience working on AI‑native or automation‑first products
- Open source: Active contributions to open source projects or a visible technical portfolio