Full-Stack Engineer – AI-Native, Infrastructure & AWS

withRemote
India

Location: Remote

Employment Type: Full-Time, Permanent

Working Days: Monday to Saturday

Experience: 5 to 10 yrs

Salary: ₹20 LPA – ₹30 LPA

Read This FirstThis 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 productionInfrastructure, 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 focusHow We Expect You to Work — AI-NativeAI 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 workflowsTech StackFrontend: 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
  • Strong 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-holdingGood 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 productsWhat 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

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