AI Consultant with RAG experience
We are looking for a senior AI consultant with deep experience in Retrieval Augmented Generation (RAG) systems to advise on architecture, backend setup, and implementation strategies.
This is a consultation role focused on design, planning, and architecture implementation guidance.
Scope of Consultation & Deliverables:
• Design and validate RAG system architecture, including backend services, APIs, and integration points
• Advise on document ingestion pipelines, chunking strategies, and embedding approaches
• Recommend vector database selection and implementation best practices for performance, scalability, and reliability
• Guide on LLM selection, prompting strategy, and context management
• Provide strategies to reduce hallucinations and improve answer reliability
• Suggest backend patterns for orchestration, caching, scaling, logging, and monitoring
• Recommend deployment, security, and data privacy best practices for production systems
• Advise on cost, latency, and scalability optimizations
• Review evaluation metrics for RAG quality and accuracy
Deliverables:
• Complete system architecture diagrams and documentation
• Backend setup guidance with production-ready patterns
• Ready-to-work boilerplate for RAG pipeline implementation
Ideal Consultant Profile:
• Strong background in Python-based AI systems and backend architecture
• Proven experience designing and architecting RAG pipelines in production
• Hands-on experience with LangChain, LlamaIndex, or custom RAG frameworks
• Deep understanding of embeddings, vector search, retrieval strategies, and backend integrations
• Experience consulting on real-world AI systems with actionable implementation guidance
• Able to communicate clearly and provide concrete recommendations for architecture, backend setup, and boilerplate
Engagement Details:
• Consultation via calls, written feedback, architecture review sessions, and implementation guidance
• Short-term engagement with potential follow-up sessions
• Flexible hours
Budget:
Open to hourly or fixed consultation packages
How to Apply:
In your proposal, briefly explain:
• One RAG system you have consulted on or architected
• Key trade-offs you helped a team decide on
• Your approach for backend architecture, RAG reliability, and boilerplate setup
Those with new profiles looking to get some traction on the platform are encouraged to apply
Apply Now
Apply Now
This is a consultation role focused on design, planning, and architecture implementation guidance.
Scope of Consultation & Deliverables:
• Design and validate RAG system architecture, including backend services, APIs, and integration points
• Advise on document ingestion pipelines, chunking strategies, and embedding approaches
• Recommend vector database selection and implementation best practices for performance, scalability, and reliability
• Guide on LLM selection, prompting strategy, and context management
• Provide strategies to reduce hallucinations and improve answer reliability
• Suggest backend patterns for orchestration, caching, scaling, logging, and monitoring
• Recommend deployment, security, and data privacy best practices for production systems
• Advise on cost, latency, and scalability optimizations
• Review evaluation metrics for RAG quality and accuracy
Deliverables:
• Complete system architecture diagrams and documentation
• Backend setup guidance with production-ready patterns
• Ready-to-work boilerplate for RAG pipeline implementation
Ideal Consultant Profile:
• Strong background in Python-based AI systems and backend architecture
• Proven experience designing and architecting RAG pipelines in production
• Hands-on experience with LangChain, LlamaIndex, or custom RAG frameworks
• Deep understanding of embeddings, vector search, retrieval strategies, and backend integrations
• Experience consulting on real-world AI systems with actionable implementation guidance
• Able to communicate clearly and provide concrete recommendations for architecture, backend setup, and boilerplate
Engagement Details:
• Consultation via calls, written feedback, architecture review sessions, and implementation guidance
• Short-term engagement with potential follow-up sessions
• Flexible hours
Budget:
Open to hourly or fixed consultation packages
How to Apply:
In your proposal, briefly explain:
• One RAG system you have consulted on or architected
• Key trade-offs you helped a team decide on
• Your approach for backend architecture, RAG reliability, and boilerplate setup
Those with new profiles looking to get some traction on the platform are encouraged to apply
Apply Now
Apply Now