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 quick, 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 rsum. Send us:

1. A few sentences on the hardest backend/infra problem you’ve owned end to end, and how you solved it.
2. One concrete example of how you use AI to 10x your work — what you do, what it replaced.
3. Links to anything you’ve built (GitHub, projects, systems you’re proud of).

We move fast on candidates who are clearly a fit.

📌 Senior Backend / Infrastructure Engineer (AI-Native) (Bengaluru)
🏢 withRemote Solutions
📍 Bengaluru