Lead FullStack Engineer

Team Scope: Backend · Frontend · AI/ML · DevOps

Experience Level: 6–10 Years

Key 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)

• Own reliability and cost tradeoffs for AI features in production — latency, token budgets, fallback strategies

Team Leadership

• 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

● 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, 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