Location: Remote

Employment Type: Full-Time, Permanent

Working Days: Monday to Saturday

Experience: 5 to 10 yrs

Salary: ₹20 LPA – ₹30 LPA

Read This First

This is not a maintenance or ticket-picking role.

We’re looking for one engineer who can take a feature from a one-line idea to live production — UI, API, database, integrations, infrastructure and deployment .

You should think in whole systems, ship fast, use AI as a core engineering multiplier, and understand how application-level decisions affect performance, scalability, reliability and infrastructure costs .

We work hard here: roughly 12 hours a day, 6 days a week . This is suited to builders who want high ownership, autonomy and the opportunity to build at speed.

What You’ll OwnEnd-to-End Product Engineering

- Build user-facing products using React, Next.js, TypeScript and Tailwind CSS
- Build backend APIs, business logic, data models and integrations using Node.js / Express.js
- Own features across the entire lifecycle — UI → API → database → infrastructure → deployment
- Build AI-powered product features using LLMs, retrieval, agents and MCP
- Make product and architecture decisions and remain accountable for the outcome
- Deploy, monitor, debug and maintain your features in production

Infrastructure, Reliability & Scalability
- Own the reliability and scalability layer of the product
- Manage and optimize AWS and Supabase production infrastructure
- Build and maintain infrastructure using Infrastructure-as-Code
- Operate and improve systems with significant active production usage
- Review how new features affect database load, CPU, memory, network usage and overall system performance
- Guide engineering decisions to prevent fragile architecture, bottlenecks and single points of failure
- Identify infrastructure sustainability, efficiency and cost risks before they become problems




- Own production monitoring, observability, deployments, incident response and reliability
- Improve system capacity and resilience as product usage scales
- Contribute to backend engineering and technical debt where infrastructure work alone does not require full-time focus

How We Expect You to Work — AI-Native AI is not a checkbox here. It should be part of how you engineer.
- Use Claude, AI agents and orchestrated workflows daily for coding, debugging, testing, infrastructure automation, documentation and repetitive engineering tasks
- Build your own agentic workflows and automations , rather than only consuming AI tools
- Use AI across the full development lifecycle — from understanding requirements to deployment and production troubleshooting
- Integrate AI capabilities directly into the product using LLM APIs, retrieval, vector search, agents and MCP
- Apply strong engineering judgment to validate AI-generated code, architecture and infrastructure changes
- Demonstrate a measurable improvement in engineering velocity through AI-native workflows

Tech Stack Frontend: React, Next.js, TypeScript, Tailwind CSS

Backend: Node.js, Express.js, Next.js API routes, FastAPI where relevant

Database: PostgreSQL, Supabase, pgvector, Redis

AI: Claude / LLM APIs, RAG, agents, MCP, vector search

Integrations: REST, GraphQL, WebSockets, OAuth2, JWT, webhooks

Infrastructure: AWS, Docker, Infrastructure-as-Code, CI/CD, GitHub Actions

Vector Stores: Pinecone, Weaviate, ChromaDB,



Qdrant

AI/ML Adjacent: Hugging Face, vLLM, Ollama, MLflow, W&B;

Must-Have Skills

- 5+ years of production full-stack engineering experience
- Robust React, Next.js, TypeScript, Node.js and Express.js
- Strong PostgreSQL / Supabase and Redis experience
- Strong understanding of APIs, integrations, authentication and system architecture
- Hands-on AWS production experience
- Strong Infrastructure-as-Code experience, preferably Terraform
- Experience operating production systems at scale / with significant active usage
- Strong understanding of database performance, CPU/resource utilization, scalability and reliability
- Experience with Docker, CI/CD, monitoring and observability
- Experience building AI-powered product features using LLM APIs, RAG/vector search or agents
- Startup / early-stage experience with ambiguous requirements and end-to-end ownership
- Demonstrated use of Claude, AI agents or similar tools as a core engineering workflow
- Ability to independently take ownership of production systems without extensive training or hand-holding

Good to Have
- Kubernetes and deeper infrastructure ownership
- AWS networking and security
- Cloud cost optimization and capacity planning
- Redis caching / Pub/Sub
- WebSockets, gRPC and streaming
- Python / FastAPI
- Dedicated vector databases
- React Native
- Model serving / MLOps
- Kafka / RabbitMQ
- Experience with high-scale SaaS, consumer or AI products

What You Get
- End-to-end ownership across product, engineering and infrastructure
- A small, fast, high-trust team with minimal bureaucracy
- Opportunity to build and scale the product from the ground up
- Direct impact on architecture, reliability and product direction
- An environment where AI-native engineering is the standard, not an experiment

📌 Full-Stack Engineer – AI-Native, Infrastructure & AWS (India)
🏢 withRemote
📍 India