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About the jobPosition: Product Engineer (AI Infrastructure)Location: Remote (India)Experience: 0–2 YearsType: 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 KatonicWe 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 OverviewWe'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 OnThe 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 LLMsNVIDIA NIM: Enterprise-grade model deploymentModel Zoo: 250+ models - LLaMA, Mistral, DeepSeek, CodeLLaMA, and moreFine-tuning Pipeline: LoRA, QLoRA, full fine-tuning on customer dataGPU Orchestration: Multi-tenant GPU allocation across Kubernetes clustersAuto-scaling: Handle traffic spikes without manual interventionGuardrails: Safety, compliance, and quality enforcement at inference timeYour ResponsibilitiesLearn to deploy and test LLM serving infrastructure (vLLM, SGLang, NIM)Test fine-tuning pipelines - LoRA, QLoRA, and full fine-tuning workflowsRun benchmarks - measure latency, throughput, memory usage, fine-tuning timeValidate new models before production (LLaMA, Mistral, DeepSeek)Test GPU allocation and auto-scaling under real workloadsValidate fine-tuned model quality against base modelsIdentify and report inference failures, OOM errors, and performance issuesDocument deployment procedures and test resultsLearn from senior engineers while contributing from day oneWho You AreMust 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 brokeNice to Have:• Coursework or projects in ML/deep learning• Exposure to Hugging Face, PyTorch, or TensorFlow• 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 BecomeIn 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, DeepSeek)• GPU optimization and Kubernetes orchestration• Understanding of sovereign AI and compliance requirementsThis is the launchpad for ML engineering, MLOps, or platform engineering roles at top AI companies.Soft SkillsProblem-solving mindsetStrong communication skillsOwnership and accountabilityAbility to learn fast and adapt to new technologiesWhat we offerOpportunity to work at the forefront of Generative AI and Agentic AIFully remote - work from anywhere in IndiaHealth insuranceAccess to GPUs for learning and experimentationMentorship from experienced ML engineersLearning budget for courses and certificationsGlobal exposure - collaborate with teams in Sydney, Singapore, DubaiPlease apply only if you match the criteria.To apply, please fill out this form: filling out the form, your application is not complete.Katonic AI is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all.