AI/ML RAG-Based Solution Development
Budget: $10 – $30 USD
We are looking for an experienced freelancer to develop a Proof of Concept (PoC) for an AI/ML solution incorporating **Retrieval-Augmented Generation (RAG)**. The goal is to build a prototype that combines machine learning with contextual data retrieval to generate accurate, relevant, and dynamic responses.
**Objectives:**
* Develop an AI/ML prototype enhanced with RAG architecture
* Enable the system to retrieve relevant information from a knowledge base and generate intelligent outputs
* Validate the effectiveness, accuracy, and scalability of the solution
* Demonstrate real-world use cases such as Q&A systems, document search, or intelligent assistants
**Scope of Work:**
* Understand project requirements and define system architecture
* Design and implement a RAG pipeline (retriever + generator)
* Prepare and preprocess datasets / documents (PDFs, text, structured data, etc.)
* Implement vector database (e.g., FAISS, Pinecone, Chroma, etc.)
* Integrate embedding models and LLM (OpenAI, open-source models, etc.)
* Develop and train/tune models where necessary
* Evaluate system performance (accuracy, relevance, latency)
* Build a simple demo interface or API to showcase functionality
* Document the entire workflow and architecture
**Deliverables:**
* Working RAG-based AI/ML prototype
* Source code with clear documentation
* Configured vector database and retrieval pipeline
* Performance evaluation report (retrieval + generation quality)
* Final report with insights and next-step recommendations
* (Optional) Demo UI or API endpoint
**Required Skills:**
* Strong experience in AI/ML and NLP
* Hands-on experience with RAG pipelines
* Proficiency in Python and ML frameworks
* Experience with vector databases (FAISS, Pinecone, Weaviate, etc.)
* Familiarity with LLMs (OpenAI API, Hugging Face models, etc.)
* Knowledge of embeddings and semantic search
**Preferred Qualifications:**
* Experience building chatbots or knowledge-based AI systems
* Familiarity with LangChain / LlamaIndex or similar frameworks
* Experience deploying AI systems (cloud or local)
* Understanding of prompt engineering and optimization'
**Objectives:**
* Develop an AI/ML prototype enhanced with RAG architecture
* Enable the system to retrieve relevant information from a knowledge base and generate intelligent outputs
* Validate the effectiveness, accuracy, and scalability of the solution
* Demonstrate real-world use cases such as Q&A systems, document search, or intelligent assistants
**Scope of Work:**
* Understand project requirements and define system architecture
* Design and implement a RAG pipeline (retriever + generator)
* Prepare and preprocess datasets / documents (PDFs, text, structured data, etc.)
* Implement vector database (e.g., FAISS, Pinecone, Chroma, etc.)
* Integrate embedding models and LLM (OpenAI, open-source models, etc.)
* Develop and train/tune models where necessary
* Evaluate system performance (accuracy, relevance, latency)
* Build a simple demo interface or API to showcase functionality
* Document the entire workflow and architecture
**Deliverables:**
* Working RAG-based AI/ML prototype
* Source code with clear documentation
* Configured vector database and retrieval pipeline
* Performance evaluation report (retrieval + generation quality)
* Final report with insights and next-step recommendations
* (Optional) Demo UI or API endpoint
**Required Skills:**
* Strong experience in AI/ML and NLP
* Hands-on experience with RAG pipelines
* Proficiency in Python and ML frameworks
* Experience with vector databases (FAISS, Pinecone, Weaviate, etc.)
* Familiarity with LLMs (OpenAI API, Hugging Face models, etc.)
* Knowledge of embeddings and semantic search
**Preferred Qualifications:**
* Experience building chatbots or knowledge-based AI systems
* Familiarity with LangChain / LlamaIndex or similar frameworks
* Experience deploying AI systems (cloud or local)
* Understanding of prompt engineering and optimization'