AI Engineer LLM Fine-Tuning & Reasoning Systems

We're building the future of Autonomous Data Intelligence at CyberPod AIand were looking for a deeply technical, hands-on AI Engineer to push the boundaries of whats possible with Large Language Models (LLMs).

This role is for someone whos already been in the trenches: fine-tuned foundation models, experimented with quantization and performance tuning, and knows PyTorch inside out. If youre passionate about optimizing LLMs, crafting efficient reasoning architectures, and contributing to open-source communities like Hugging Face, this is your playground.

What You'll Do

• Fine-tune Large Language Models (LLMs) on custom datasets for specialized reasoning tasks.
• Design and run benchmarking pipelines across accuracy, speed, token throughput, and energy efficiency.
• Implement quantization, pruning, and distillation techniques for model compression and deployment readiness.
• Evaluate and extend agentic RAG (Retrieval-Augmented Generation) pipelines and reasoning agents.
• Contribute to SOTA model architectures for multi-hop, temporal, and multimodal reasoning.
• Collaborate closely with the data engineering, infra, and applied research teams to bring ideas from paper to production.
• Own and drive experiments, ablations, and performance dashboards end-to-end.

Requirements

• 3+ years of hands-on experience working with deep learning and large models, particularly LLMs.
• Strong understanding of PyTorch internals: autograd, memory profiling, efficient dataloaders, mixed precision.
• Proven track record in fine-tuning LLMs (e.g., LLaMA, Falcon, Mistral, Open LLaMA, T5, etc.) on real-world use cases.
• Benchmarking skills: can run standardized evals (e.g., MMLU, GSM8K, HELM, TruthfulQA) and interpret metrics.
• Deep familiarity with quantization techniques: GPTQ, AWQ, QLoRA, bitsandbytes, and low-bit inference.
• Working knowledge of Hugging Face ecosystem (Transformers, Accelerate, Datasets, Evaluate).
• Active Hugging Face profile with at least one public model/repo published.
• Experience in training and optimizing multi-modal models (vision-language/audio) is a big plus.
• Published work (arXiv, GitHub, blogs) or open-source contributions preferred.

If you are passionate about AI and want to be a part of a dynamic and innovative team, then ZySec AI is the perfect place for you. Apply now and join us in shaping the future of artificial intelligence.