LLM Engineer - AI-Assisted RTL Integration

Job Overview

LLM Engineer with expertise in prompt engineering, dataset creation, fine-tuning, and deployment of on-premises open-source LLMs for RTL (Register Transfer Level) design. The ideal candidate will work closely with RTL domain experts to develop and optimize AI-assisted RTL integration workflows. The role involves prompt engineering, output validation and re-prompting, fine-tuning of LLMs when necessary, and building datasets to enhance model accuracy using the latest AI/ML technologies.

Key Responsibilities

• LLM Deployment & Integration:
  
  
  
  • Deploy and optimize open-source LLMs on-premises for RTL integration.
  
  
  • Develop custom pipelines for LLM-assisted RTL design, analysis, and verification.
  
  
  • Work with RTL experts to fine-tune prompts for best performance.
  
  


• Prompt Engineering & Optimization:
  
  
  
  • Design, refine, and test effective prompts for RTL integration tasks.
  
  
  • Continuously evaluate LLM responses and develop strategies for output validation.
  
  
  • Implement re-prompting techniques to improve accuracy and efficiency.
  
  


• Dataset Creation & Fine-Tuning:
  
  
  
  • Identify gaps in model accuracy and develop datasets for model retraining.
  
  
  • Collect, clean, and curate RTL-specific datasets to improve model performance.
  
  
  • Fine-tune LLMs using state-of-the-art training frameworks (e.g., PyTorch, TensorFlow, Hugging Face).
  
  
  • Experiment with latest AI/ML techniques to optimize LLM efficiency for RTL workflows.
  
  


• Model Validation & Performance Tuning:
  
  
  
  • Implement evaluation metrics to measure model performance in RTL tasks.
  
  
  • Conduct benchmarking and performance tuning to ensure model accuracy.
  
  
  • Develop feedback loops for continuous improvement of LLM-assisted RTL processes.
  
  


• Collaboration & Research:
  
  
  
  • Work closely with RTL engineers to understand domain challenges.
  
  
  • Stay up to date with the latest advancements in AI/ML and hardware design automation.
  
  
  • Evaluate and implement state-of-the-art LLM architectures for RTL-specific applications.
  
  

Key Skills

• Strong expertise in LLMs – Open-source models like Llama, Falcon, Mistral, or GPT-based architectures.


• Experience in fine-tuning LLMs using PyTorch, TensorFlow, or Hugging Face.


• Prompt engineering expertise – Ability to craft optimized prompts for RTL tasks.


• Understanding of RTL design (basic knowledge preferred) and how AI can assist in hardware workflows.


• Experience in data collection, preprocessing, and synthetic dataset creation for fine-tuning.


• Familiarity with LLM inference optimization techniques (e.g., quantization, pruning).


• Strong coding skills in Python and frameworks like LangChain, LlamaIndex, or OpenAI APIs.


• Experience with containerization (Docker, Kubernetes) and on-prem model hosting.

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