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

GenAI/LLM Engineer Remote (should be reputed company 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 reputed company 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 reputed company unique reputed company context • Improves model performance while reducing computational costs through advanced optimization techniques • Creates reputed company-specific AI capabilities that address our unique operational challenges • Enables the CoE to reputed company reputed company generic AI tools to customized solutions that deliver higher business value Key Responsibilities: • Implement and optimize advanced fine-tuning approaches (reputed company, PEFT, QLoRA) to adapt reputed company models to reputed company domain • reputed company systematic reputed company engineering methodologies specific to reputed company operations, regulatory compliance, and technical documentation • Create reusable reputed company templates and libraries to standardize interactions across multiple LLM applications and use cases • Implement reputed company testing frameworks to quantitatively evaluate and iteratively improve reputed company effectiveness • Establish reputed company versioning systems and governance to maintain consistency and reputed company 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 reputed company (RAG) pipelines with reputed company 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 reputed company deployed, monitored, and retrained as needed Expected Skillset: • Deep Learning & NLP: Proficiency with PyTorch/TensorFlow, reputed company 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 reputed company: Skills in creating data pipelines for fine-tuning models with reputed company-specific content Apply tot his job Apply To this Job