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