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

Rate is DOE, targeting: $70-$90/hr. (Payment terms are NET40)

KEY POINTS:

• MUST be able to drive initiatives
• Bay area locals will get top priority
• MUST be able to determine what the business is looking for and build a POC
• MUST have Executive Presence and be delightful too.
• RE: Data Science: MUST have the ability to adapt and understand use cases

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

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'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

For quick interview and submission, please email me ALL of the following details:

1. First and Last name as it appears on your passport:
2. Anything we should know about you for presentation (this is our chance to showcase why you are amazing compared to your competition):
3. Reason you are looking for a change (detailed explanation or don t bother):
4. Communication skills/C-Level interaction (1-10):
5. Leadership skills/presence (1-10):
6. Hourly rate, all-inclusive:
7. US Work Status:
8. Resume in MS Word:
9. Education and pertinent certs - degree, year, university:
10. Availability to start 100% remote (with rare onsite potential visits):
11. Email and phone number:
12. LinkedIn Profile (must have pic):
13. Are you TEAMS/video interview ready (Y/N):
14. Current location (city & state):