GenAI/LLM Engineer (NLP, TensorFlow, PyTorch SME)

GenAI/LLM Engineer (NLP, TensorFlow, PyTorch SME)

San Francisco, Bay Area, CA

Duration: Six months may extend to 12 months

Must be in the Greater Bay area – or in California

Domain: utilities

MUST be a US Citizen or GC holder

I mplementing 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's 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'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

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