GenAI/LLM Engineer

Responsibilities:

• Implement and optimize advanced fine-tuning approaches (LoRA, PEFT, QLoRA) to adapt foundation models


• 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

Oscar Associates Limited (US) is acting as an Employment Business in relation to this vacancy.