Senior Backend / Infrastructure Engineer (AI-Native)

Job Title: Senior Backend / Infrastructure Engineer (AI-Native) Location: Remote Working Days: Monday to Saturday Employment Type: Full Time, Permanent Salary: 24LPA to 60 LPA Read this first This is not a maintenance role. Were not looking for someone to pick up tickets from a backlog and close them at a comfortable pace. Were looking for one person who can do the work of a small team because they think in systems, ship fast, and have figured out how to use AI as a force multiplier instead of a novelty.

If youve spent the last year quietly building Claude, agents, and AI workflows into how you actually work and you’re shipping 5–10x what you used to — this is for you. If “AI-native” is a line on your rsum but not a habit, it isn’t. We work hard here: roughly 12 hours a day, 6 days a week .

We’re saying that up front because it’s the truth and we’d rather you self-select than be surprised. This suits people in builder mode who want equity-grade ownership and the speed that comes with it. It does not suit people optimizing for balance right now, and that’s a completely valid choice — just not this role.

What you’ll own You are the backend, infra, DevOps, and database function — end to end. No hand-offs, no “that’s another team’s problem.” Design, build, and operate backend services and APIs that are fast, observable, and don’t fall over. Own the infrastructure: provisioning, CI/CD, containers, deployments, scaling, cost.

Own the data layer: schema design, query performance, migrations, backups, integrity. Own reliability: monitoring, alerting, incident response, and the boring discipline that prevents 3am pages. Make architecture decisions and live with them.

You decide, you build, you’re accountable for the outcome. How we expect you to work (the AI-native part) This is the differentiator and we’re serious about it. You use Claude / AI agents / orchestrated workflows as a daily tool to compress the work — scaffolding services, writing and reviewing migrations, debugging, generating tests, drafting infra-as-code, automating the repetitive.

You can build the automation, not just consume it : chaining tools, writing agentic workflows, wiring AI into your dev and ops loop so the system does more of the grunt work over time. You exercise judgment over the output. AI accelerates you; it doesn’t think for you.

You know when to trust it and when to throw the answer away. Net effect: you ship at a pace that looks unreasonable to someone working the old way. Tech stack What you’ll be working in day to day: Backend: Node.js, Express.js Databases: PostgreSQL (incl. pgvector), Redis Vector/embeddings: pgvector, plus dedicated stores — Pinecone, Weaviate, ChromaDB, Qdrant AI / NLP: Hugging Face, spaCy, NLTK Model serving & MLOps: vLLM, Ollama, MLflow, Weights & Biases APIs & auth: REST, GraphQL, gRPC, WebSockets; OAuth2, JWT, rate limiting (Redis / API Gateway) Messaging & streaming: Redis Pub/Sub, RabbitMQ, Kafka Infra & DevOps: AWS, Docker, Kubernetes, CI/CD (GitHub Actions, Jenkins) AI integration: Model Context Protocol (MCP) Must-have skills 5+ years building and operating production backend systems.

Node.js + Express.js — deep, production-grade experience as your primary backend stack. PostgreSQL + Redis — schema design, query performance, migrations, caching, and reasoning about consistency at scale. Vector databases & embeddings — hands-on with pgvector or a dedicated vector DB; you understand how retrieval actually works, not just the API.

API design & auth — REST and GraphQL, OAuth2/JWT, and rate limiting (Redis or API Gateway). Cloud + containers — AWS, Docker, and CI/CD with GitHub Actions or Jenkins. At least one message/streaming system — Redis Pub/Sub, RabbitMQ, or Kafka — used in production.

Model Context Protocol (MCP) — you’ve built or integrated MCP servers/tools, or can clearly demonstrate you’ll get there fast. Startup background — early-stage, small teams, ambiguous specs, real ownership. Demonstrated AI-native workflow — concrete examples of using Claude/agents to 10x specific work.

Good-to-have skills Kubernetes at production scale (orchestration, autoscaling, real ops). gRPC and WebSockets for low-latency / real-time services. Python + FastAPI — useful when ML and backend overlap. NLP tooling — Hugging Face, spaCy, NLTK.

Managed / dedicated vector stores — Pinecone, Weaviate, ChromaDB, Qdrant. Model serving & MLOps — vLLM, Ollama, MLflow, Weights & Biases. Kafka at high throughput — event-driven architectures and stream processing.

Security-conscious instincts — you think about the attack surface before someone makes you. What you get Real ownership and the autonomy to match — you make the calls in your domain. A small, fast, high-trust team with no bureaucracy between you and shipping.

The chance to build the technical foundation of the company rather than inherit someone else’s. How to apply Don’t send a generic resume. Send us: A few sentences on the hardest backend/infra problem you’ve owned end to end, and how you solved it.

One concrete example of how you use AI to 10x your work — what you do, what it replaced. Links to anything you’ve built (GitHub, projects, systems you’re proud of). We move fast on candidates who are clearly a fit.