PDSSOFT INC.

Develop and optimize advanced fine-tuning and prompt engineering techniques for LLMs specific to utility domain, and collaborate on model deployment and monitoring. | Proficiency in PyTorch/TensorFlow, advanced LLM training, GPU hardware knowledge, LLMOps experience, and ability to adapt research techniques to production. | GenAI/LLM Engineer Location: Remote Duration: Long Term Job Description: • Implement and optimize advanced fine-tuning approaches (LoRA, PEFT, QLoRA) to adapt foundation models to PG&E's domain • Develop systematic prompt engineering methodologies specific to utility operations, regulatory compliance, and technical documentation • Create reusable prompt templates and libraries to standardize interactions across multiple LLM applications and use cases • Implement prompt testing frameworks to quantitatively evaluate and iteratively improve prompt effectiveness • Establish prompt versioning systems and governance to maintain consistency and quality across applications • Apply model customization techniques like knowledge distillation, quantization, and pruning to reduce memory footprint and inference costs • Tackle memory constraints using techniques such as sharded data parallelism, GPU offloading, or CPU+GPU hybrid approaches • Build robust retrieval-augmented generation (RAG) pipelines with vector databases, embedding pipelines, and optimized chunking strategies • Design advanced prompting strategies including chain-of-thought reasoning, conversation orchestration, and agent-based approaches • Collaborate with the MLOps engineer to ensure models are efficiently deployed, monitored, and retrained as needed • Deep Learning & NLP: Proficiency with PyTorch/TensorFlow, Hugging Face Transformers, DSPy, and advanced LLM training techniques • GPU/Hardware Knowledge: Experience with multi-GPU training, memory optimization, and parallelization strategies • LLMOps: Familiarity with workflows for maintaining LLM-based applications in production and monitoring model performance • Technical Adaptability: Ability to interpret research papers and implement emerging techniques (without necessarily requiring PhD-level mathematics) • Domain Adaptation: Skills in creating data pipelines for fine-tuning models with utility-specific content "