Posted:2 days ago|
Platform:
Work from Office
Full Time
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.
Zysec Ai
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