Description

Core Experience

• Hands-on experience deploying open-source LLMs such as Meta Llama 3 and Mistral / Mixtral in on-prem or private environments
• Strong proficiency in Python for LLM inference, prompt engineering, and integration
• Experience with CPU-based inference, model quantization, and performance tuning

Vector Databases & RAG

• Practical experience with open-source vector databases such as Qdrant, Chroma, Milvus, or pgvector
• Proven implementation of Retrieval-Augmented Generation (RAG) pipelines
• Experience generating and managing embeddings and metadata filtering

Security & Governance

• Understanding of data privacy, air-gapped deployments, and enterprise security requirements
• Experience implementing access controls and audit logging

Nice to Have

• Experience with LangChain or LlamaIndex
• Exposure to Rust, Go, or C++ for high-performance services
• Familiarity with Docker and Kubernetes for on-prem deployments
• Knowledge of inference frameworks (e.g., vLLM, llama.cpp, Hugging Face Transformers)
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• Prior work in regulated or enterprise environments

Deliverables

• Reference architecture and deployment guidance
• Working prototype (LLM + vector DB + RAG)
• Documentation and knowledge transfer to internal teams

Key Skills

Education

Any Graduate