[xrecnet] LLM Engineer WITH AI @ CA - need local profiles here.

 

 Kindly share only local to CA profiles here.

 

 

 

 

 

LLM Engineer – AI-Assisted RTL Integration
Location: Bay Area, CA (Onsite Only)- local required
Industry: Semiconductor / AI / EDA

 

 

Mandate skills , LLMS , FALCON , LLAMA , GPT , PYTORCH , TENSORFLOW , HUGGING FACE , RTL ,LangChain, LlamaIndex, or OpenAI APIs.quantization, pruning,fine-tuning,RTL tasks.LLMs using PyTorch, TensorFlow, or Hugging Face,falcon ,mistral

 

Required Skills	Rating (Scale 1-10)	Work Experience (Years)	Last Used (Year)
Overall IT experience
·       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.

 

 

 

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:
1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

 

 

 

Regards

Pavan

VDart Inc

Ph: (470) 251-2584 Ext:1866

Email: Pavankumar.s@vdartinc.com

Website: https://vdart.com

 

 

 

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