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Protocol Intelligence
Data-driven signals on your job's competitivenessNiche LLM fine-tuning role with mandatory certifications and remote work reduces applicant density.
Specialized LLM, RLHF, and quantization expertise limits transferability across unrelated domains.
Mandatory cloud ML certification, explicit experience range, and specialized LLM tooling create strict filtering.
Job Description
Structured overview of role & requirementsAbout This Role
Own and lead LLM fine-tuning projects using PEFT techniques like LoRA, QLoRA, Prefix Tuning, and Prompt Tuning for open-source models (e.g., Llama, Mistral).
Curate, clean, and prepare high-quality datasets with robust automation and human-in-the-loop validation for training.
Design and execute advanced model evaluation benchmarks and manage distributed deep learning training jobs across multi-GPU environments.
Minimum Requirements
4 to 8 years of core data science or advanced machine learning engineering experience with at least 3 years dedicated to NLP system training and fine-tuning.
Expert-level proficiency in Python, PyTorch, transformer architectures (Hugging Face, Accelerate, PEFT), and understanding of attention mechanisms and CUDA limitations.
Mandatory certification as Google Cloud Certified Professional Machine Learning Engineer or AWS Certified Machine Learning Specialty.
Work Mode: Fully Remote (Offshore); Employment Type: Contract.
Ideal Candidate Profile
Experienced practitioner in parameter-efficient LLM fine-tuning for domain-specific and industry-specific adaptations.
Strong technical command over distributed training, memory optimization (quantization), and model safety via reinforcement learning alignment.
Advanced academic background preferred (Master’s or Ph.D.) focused on neural network text models or related fields.