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

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