Full Stack AI Engineer

The Role

We're hiring a Full Stack AI Engineer to join us full-time, on-site in Indore. You'll work directly with the founder on AI agent systems, automation workflows, and full-stack applications for both client projects and our own SaaS products.

You'll be shipping code across the stack:

• Backend agentic systems in Python (Django/FastAPI) with LangChain and LangGraph
• n8n workflow automation orchestrating AI agents, APIs, and business logic
• API integrations with external platforms.
• Vector search and memory systems using Postgres + pgvector
• Frontend dashboards and admin panels in Next.js + Tailwind
• Deployment on Docker, Hostinger VPS, AWS

What You'll Actually Do

• Build production AI agents using LangChain / LangGraph (tool use, routing, memory, multi-step reasoning)
• Develop n8n workflows that orchestrate AI calls, API integrations, and scheduled jobs
• Write clean, async Python backends with FastAPI, Celery, Redis
• Integrate third-party APIs (REST, OAuth, webhooks) reliably with proper error handling and retries
• Build lean frontend dashboards in Next.js + Tailwind to surface agent activity and let users interact with our systems
• Work with Postgres and pgvector for structured + semantic data
• Ship features end-to-end, from architecture discussion to deployed code
• Communicate clearly with the founder on progress, blockers, and design tradeoffs

What We're Looking For

Must have:

• 1–2 years of full-stack development experience with Python and at least one modern JS framework (React, Next.js, or similar)
• Hands-on experience with at least one LLM API (OpenAI, Anthropic, Gemini, etc.) — you've built something real with it, not just a tutorial
• Strong fundamentals in REST API design and integration
• Comfortable with Git, command line, Linux basics
• You can explain your code and your design choices clearly
• Must know to work optimum with AI Coding tools (Claude Code, Antigravity, others)

Strong plus (any of these put you ahead):

• Worked with LangChain, LangGraph, LlamaIndex, or similar frameworks
• Built or maintained n8n workflows (or similar — Make, Zapier with custom code, Temporal)
• Experience with Postgres, pgvector, Redis, Celery, or Docker
• Shipped a side project, open-source contribution, or freelance work that's actually in use
• Comfort with prompt engineering and understanding how to make LLMs reliable in production

We don't care about:

• Degree pedigree (CS / IT / self-taught — all welcome if you can ship)
• Whether you came from a service company or a startup
• Whether you have "AI/ML" formal training — practical building beats theory here