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Remote mid-level ML role but niche LLM fine-tuning skills moderate applicant density.
Core ML/LLM expertise is transferable across industries but compliance focus increases domain sensitivity.
Mandatory 4+ years and specific LLM fine-tuning tooling requirements create high shortlisting strictness.
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Design, train, fine-tune, and evaluate large language models and ML models powering AI agents and GRC AI products under direct CTO guidance.
Manage end-to-end model training pipelines, including dataset curation, fine-tuning (LoRA/QLoRA, instruction tuning, RLHF/DPO), and optimization for production deployment with a focus on accuracy, explainability, and auditability in regulated environments.
Collaborate across AI Solutions Engineering, Data Engineering, Product, and Compliance teams to ensure models meet governance, quality, and responsible AI standards and support customer-facing technical discussions.
4+ years of experience in machine learning engineering, applied NLP, or LLM training/fine-tuning roles.
Hands-on experience fine-tuning large language models using LoRA/QLoRA, full fine-tuning, instruction tuning, or RLHF/DPO.
Strong proficiency in Python and ML frameworks such as PyTorch and Hugging Face Transformers/PEFT/TRL.
Work Experience Required: 4+ years; Location: Fully Remote; Travel: Minimal.
Experienced ML engineer able to translate CTO's technical strategy into production-ready models with rigorous evaluation and compliance focus.
Comfortable working in highly regulated domains such as governance, risk, compliance, audit, or finance with knowledge of AI governance and model risk management.
Capable of managing full training infrastructure and collaborating closely with senior technical leadership and cross-functional teams in a fast-paced product environment.