GenAI / LLM Engineer - Remote (should be able to work on PST time zones)

GenAI/LLM Engineer Remote (should be able to work on PST time zones) Prefers local to bay area. Implementing GenAI requires specialized expertise in large language models. Traditional data scientists often haven't had the opportunity to dive deep into the practical intricacies of LLMs-particularly advanced fine-tuning techniques, model compression strategies, memory optimization approaches, and specialized training workflows. This role requires a hands-on deep learning practitioner comfortable with modern frameworks and libraries specific to LLM development. Enables domain-specific fine-tuning of models to Client unique utility context Improves model performance while reducing computational costs through advanced optimization techniques Creates Client-specific AI capabilities that address our unique operational challenges Enables the CoE to move beyond generic AI tools to customized solutions that deliver higher business value Key Responsibilities: Implement and optimize advanced fine-tuning approaches (LoRA, PEFT, QLoRA) to adapt foundation models to Client 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 Expected Skillset: 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