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Open roles

AI / ML

AI/ML Engineer - Immediate joiners only.

Xuman.AI

FresherOn-site · IndiaFull-timeListed 2h ago
Apply on LinkedIn

How to stand out for AI/ML Engineer - Immediate joiners only. at Xuman.AI

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Experience Fresher-friendly - open to freshers / early-career candidates

About the role

structured by ORI

Xuman.Ai is live on both app stores. Real people are using it.

What you will do

  • The AI that greets, talks and acts for a user inside a live app, on both stores.
  • The agent layer: tool calling, orchestration, memory, routing between models, and what happens when a tool fails or a model is slow or wrong.
  • Voice and real-time pipelines: speech in, reasoning, speech out, inside a latency budget.
  • Safety and content: the checks before an action or a piece of content reaches a person; bios, captions and insights that read as the person, not the model.
  • The ML foundation: the signals we capture, the data behind them, evals on every change, cost per conversation known, and the first learned models when the data allows.

What they are looking for

  • You have built with LLMs and shipped something agentic to real users, not a demo.
  • Agents and tool calling in production, including what happens when a tool fails or a model is slow or wrong.
  • Multi-step or multi-agent orchestration you designed. LangGraph or equivalent, and the judgement to keep it simple.
  • Real machine learning behind you: a model you trained, fine-tuned or evaluated, and you can say what the numbers meant.
  • Evals you built yourself, so a change is proven rather than asserted.
  • Python in production, and comfort in a TypeScript service. Ours is a NestJS gateway.
  • Structured outputs, streaming and function calling as daily tools, across more than one model provider.
  • Model cost and latency treated as engineering problems, not surprises.
  • You follow the field as it moves, and you have changed something you built because of what shipped last month.
  • You already build with Claude, Copilot or ChatGPT every day.

Nice to have

  • Real-time voice on LiveKit.
  • Retrieval done right, and the judgement to know when not to use it.
  • Safety and moderation systems in production.
  • Recommendation or matching systems in production.
  • Data pipelines at real volume.

Benefits

  • Work that is yours end to end, not tickets off someone else's board.
  • Your work in real hands the same week, on a product that is live today.
  • Pay that grows with every milestone we hit.
  • Growth that is claimed, not handed out. More opens as you carry more.
  • You're joining before the growth. What you build now is the foundation, and inside the company it will always be known as yours.
LLMsagentic systemstool callingmulti-step or multi-agent orchestrationmachine learningevaluation (evals)PythonTypeScriptLangGraphNestJSLiveKitClaudeCopilotChatGPT
Full posting text

Xuman.Ai is live on both app stores. Real people are using it.

What is Xuman.Ai

Xuman is building a network for on-demand human connection.

There are billions of people with knowledge, skills, and experience that could be valuable to someone else. Making that accessible is a coordination problem.

What you want to do, who’s up for it, and when you’re both available.

Xuman brings those things into one system. You discover the person, book their time, and connect. If they’re available now, that can happen immediately. And you can make your own capabilities available through the same network.

The goal is to make finding and connecting with the right human as easy as finding information.

Think about how much more people could learn, build, and accomplish if that worked at a global scale.

The stage

What's the rarest stage to join a startup?

Building from zero? Everywhere.

Joining after scale? Everywhere too.

The stage in between is where it gets interesting.

The product is built. It's live. People are using it.

Now it's about syncing what they feel with what we build.

Ship. Watch. Learn. Fix. Improve. Repeat.

What you'll own

  • The AI that greets, talks and acts for a user inside a live app, on both stores.
  • The agent layer: tool calling, orchestration, memory, routing between models, and what happens when a tool fails or a model is slow or wrong.
  • Voice and real-time pipelines: speech in, reasoning, speech out, inside a latency budget.
  • Safety and content: the checks before an action or a piece of content reaches a person; bios, captions and insights that read as the person, not the model.
  • The ML foundation: the signals we capture, the data behind them, evals on every change, cost per conversation known, and the first learned models when the data allows.

How we work

The founder sets what the product does and how it should feel. When you spot a gap, a bug or a better way, you bring it up before you build it. Once it's agreed, the technical approach is all yours. That is how we stay fast without breaking what people already use.

What you bring

  • You have built with LLMs and shipped something agentic to real users, not a demo.
  • Agents and tool calling in production, including what happens when a tool fails or a model is slow or wrong.
  • Multi-step or multi-agent orchestration you designed. LangGraph or equivalent, and the judgement to keep it simple.
  • Real machine learning behind you: a model you trained, fine-tuned or evaluated, and you can say what the numbers meant.
  • Evals you built yourself, so a change is proven rather than asserted.
  • Python in production, and comfort in a TypeScript service. Ours is a NestJS gateway.
  • Structured outputs, streaming and function calling as daily tools, across more than one model provider.
  • Model cost and latency treated as engineering problems, not surprises.
  • You follow the field as it moves, and you have changed something you built because of what shipped last month.
  • You already build with Claude, Copilot or ChatGPT every day.

What helps further

  • Real-time voice on LiveKit.
  • Retrieval done right, and the judgement to know when not to use it.
  • Safety and moderation systems in production.
  • Recommendation or matching systems in production.
  • Data pipelines at real volume.

What you get

  • Work that is yours end to end, not tickets off someone else's board.
  • Your work in real hands the same week, on a product that is live today.
  • Pay that grows with every milestone we hit.
  • Growth that is claimed, not handed out. More opens as you carry more.
  • You're joining before the growth. What you build now is the foundation, and inside the company it will always be known as yours.

How to be a Xuman

Experience the product first. It costs you a few minutes, nothing else.

Then pick your path.

Apply here, and wait for my message to book a time.

Or come find me in the app. Create your Xuman.Ai profile through

https://xuman.ai/vishal.chamaria

Put up one thing you know, do, or have a strong POV on, so people can book you. Then text me your profile link in the app, or book a time with me.

Before you book a time, two things, so I know who I'm talking to:

  • Get your blue tick. It tells me you're real.
  • Fill in your details under Settings → Personal. It tells me who you are.

Then book a time with me, whenever works.

Come as a builder, not a candidate.

Seniority level: Entry level

Employment type: Full-time

Industries: Social Networking Platforms

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