LLM/AI/Data Scientist-W2 ONLY-USC-GC

Prior Bank of America or banking/financial domain experience
LLM Engineering / AI Engineering / ML Engineering / Data Science background
Strong hands-on skills in RAG (Retrieval-Augmented Generation) frameworks
Comfortable working on internal LLM Chabot products, scaling to thousands of users
must be a critical thinker who can balance technical limitations and business requirements

LLMs & Inference:

• Experience with LLaMA 3, Mistral, and potentially Quinn
• Strong hands-on with VLLM (inference engine)
• NVIDIA Triton major plus for model serving

Core Development:

• Python 3.10+ (they use 3.12)
• Flask or FastAPI for LLM APIs
• Java (optional but preferred) backend services

Database & RAG:

• Redis + other vector databases
• SQL
• Solid RAG implementation experience (e.g., vectorized queries, business parameter tuning)

Infrastructure & Ops:

• Containerization & CI/CD Docker, OpenShift
• CI/CD tools like XLR, Datical
• Strong understanding of GPU infra and model scaling challenges
• Agile experience