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About the Role

We are seeking a consultant with expertise in on-premise Large Language Model (LLM) and Vector Database implementation. This role involves deploying open-source LLMs, building RAG pipelines, and ensuring security and governance for enterprise environments.

Responsibilities

• Deploy open-source LLMs such as Meta Llama 3 and Mistral / Mixtral in on-prem or private environments
• Implement Retrieval-Augmented Generation (RAG) pipelines
• Generate and manage embeddings and metadata filtering
• Implement access controls and audit logging
• Provide reference architecture and deployment guidance
• Develop a working prototype (LLM + vector DB + RAG)
• Conduct documentation and knowledge transfer to internal teams

Requirements

• Hands-on experience deploying open-source LLMs 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
• Practical experience with open-source vector databases
• Proven implementation of Retrieval-Augmented Generation (RAG) pipelines
• Experience generating and managing embeddings and metadata filtering
• Understanding of data privacy, air-gapped deployments, and enterprise security requirements
• Experience implementing access controls and audit logging

Skills

• Meta Llama 3
• Mistral / Mixtral
• Python
• Prompt Engineering
• CPU-based inference
• Model quantization
• Performance tuning
• Qdrant
• Chroma
• Milvus
• pgvector
• Retrieval-Augmented Generation (RAG)
• Embeddings
• Metadata filtering
• Data privacy
• Air-gapped deployments
• Enterprise security
• Access controls
• Audit logging
• LangChain
• LlamaIndex
• Rust
• Go
• C++
• Docker
• Kubernetes
• vLLM
• llama.cpp
• Hugging Face Transformers

Location

• Philadelphia

Work Type

• Contract
• Hybrid

Experience Level

• Consultant