AI Engineer - Model Adaptation & Evaluation
Core
Adapt, evaluate, and deploy open-weight language models for specialized operational use cases within autonomous security systems.
Role type
LLM Fine-Tuning Engineer
Builds
Task-specific LLM systems for security vulnerability identification and risk prioritization
Domain
Cybersecurity / Large Language Models
Deliverable
production ML models
Required skills
LLM fine-tuning, supervised fine-tuning (SFT), parameter-efficient fine-tuning (LoRA, QLoRA), dataset curation, synthetic data generation, model evaluation, Python, PyTorch, Hugging Face Transformers, GPU optimization, quantization
Preferred skills
secure/on-premise deployment, inference optimization, distributed training, red-teaming, cybersecurity background
Technologies
PyTorch, Hugging Face Transformers, LoRA, QLoRA, DPO
Responsibilities
Fine-tune and adapt open-weight LLMs for specialized use cases; Design and run post-training approaches (SFT, LoRA, QLoRA, DPO); Build and clean high-quality training and evaluation datasets; Generate and validate synthetic data; Define and implement evaluation methods beyond generic benchmarks; Analyze model regressions and failure modes; Package and serve tuned models in local or restricted environments
Seniority
Mid-Senior, hands-on IC
