Product Engineer - AI Infrastructure

About the job

- Position:
Product Engineer (AI Infrastructure)

- Location:

Remote (India)

- Experience:

0–2 Years
- Type:
Full-time

"We deployed a 70B parameter LLM for a government serving 115 million people. It runs entirely on their infrastructure. Zero data leaves their borders. That's not a demo - that's production."

Want to learn how to build systems like this? Keep reading.

About Katonic

We are a Sovereign Enterprise AI Company. Founded in Sydney in 2020, we've grown into a profitable global operation powering AI infrastructure for enterprises and governments across 11 countries. Our platform runs entirely within customer infrastructure - zero data egress, zero vendor lock-in.

Our platform - 250+ AI models, 80+ pre-built agents, ISO 27001 certified. Used by enterprises who report up to 80% increase in workflow efficiency.

Role Overview

We're hiring 2 Product Engineers for our AI Infrastructure team (internally called Adaptive Engine). This is an entry-level role where you'll learn to work on systems that deploy, serve, and fine-tune LLMs at enterprise scale. You'll start by learning, then quickly contribute to production systems that serve inference requests and run fine-tuning jobs for banks, governments, and Fortune 500 companies. If working on cutting-edge LLM infrastructure excites you, we should talk.

What You'll Work On

The Adaptive Engine is our LLM infrastructure for serving and fine-tuning. Here's what's under the hood:

- vLLM & SGLang:
High-performance inference engines for LLMs
- NVIDIA NIM:
Enterprise-grade model deployment
- Model Zoo: 250+ models - LLaMA, Mistral, Deep Seek, CodeLLaMA, and more
- Fine-tuning Pipeline:
LoRA, QLoRA, full fine-tuning on customer data
- GPU Orchestration:
Multi-tenant GPU allocation across Kubernetes clusters
- Auto-scaling:
Handle traffic spikes without manual intervention
- Guardrails:
Safety, compliance, and quality enforcement at inference time

Your Responsibilities

- Learn to deploy and test LLM serving infrastructure (vLLM, SGLang, NIM)
- Test fine-tuning pipelines - LoRA, QLoRA, and full fine-tuning workflows
- Run benchmarks - measure latency, throughput, memory usage, fine-tuning time
- Validate new models before production (LLaMA, Mistral, Deep Seek)
- Test GPU allocation and auto-scaling under real workloads
- Validate fine-tuned model quality against base models
- Identify and report inference failures, OOM errors, and performance issues
- Document deployment procedures and test results
- Learn from senior engineers while contributing from day one

Who You Are

Must Have:

• 0-2 years experience (fresh graduates with strong fundamentals welcome)

• Solid Python skills - you can write clean, working code

• Understanding of ML basics - what is a model, training vs inference, fine-tuning

• Familiarity with deep learning concepts (transformers, neural networks)

• Basic knowledge of Linux command line

• Exposure to Docker (even just tutorials or coursework)

• Curiosity about LLMs - you've played with ChatGPT, Claude, or open-source models

• Debugging mindset - you don't give up until you understand why something broke

Nice to Have:

• Coursework or projects in ML/deep learning

• Exposure to Hugging Face, PyTorch, or Tensor Flow

• Understanding of fine-tuning concepts (LoRA, transfer learning)

• Basic understanding of APIs (REST)

• Personal projects deploying or fine-tuning ML models

• Familiarity with cloud platforms (AWS/GCP)

What You'll Become

In 12 months, you'll have skills most ML engineers don't:

• LLM serving expertise - vLLM, SGLang, NVIDIA NIM (rare and in-demand)

• Fine-tuning at scale - LoRA, QLoRA, full fine-tuning on enterprise data

• Production ML infrastructure at enterprise scale

• Hands-on with latest models the day they release (LLaMA 4, Mistral, Deep Seek)

• GPU optimization and Kubernetes orchestration

• Understanding of sovereign AI and compliance requirements

This is the launchpad for ML engineering, MLOps, or platform engineering roles at top AI companies.

Soft Skills

- Problem-solving mindset
- Strong communication skills
- Ownership and accountability
- Ability to learn fast and adapt to new technologies

What we offer

- Opportunity to work at the forefront of Generative AI and Agentic AI
- Fully remote - work from anywhere in India
- Health insurance
- Access to GPUs for learning and experimentation
- Mentorship from experienced ML engineers
- Learning budget for courses and certifications
- Global exposure - collaborate with teams in Sydney, Singapore, Dubai

Please apply only if you match the criteria.

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Katonic AI is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all.