Agentic AI / RAG Engineer for Employment Law Assistant POC

Agentic AI / RAG Engineer for Employment Law Assistant POC Project Overview We are looking for an experienced **AI/LLM Engineer** to build Version 1 of an AI-powered legal assistant focused on employment law. The initial objective is to develop a functional proof of concept using **Agentic AI, Retrieval-Augmented Generation, and a legal knowledge database**. The platform should help users understand employment law, work with legal documents, receive dispute-resolution guidance, and access relevant legal updates in clear, understandable language. This is not intended to be a generic chatbot. We need a developer who understands how to build a reliable, source-grounded AI system for a legal use case, with strong attention to accuracy, traceability, data privacy, and hallucination reduction. ## Core POC Features The prototype should include: * A conversational legal assistant focused on employment law * Retrieval-Augmented Generation over legal documents, regulations, policies, precedents, and internal knowledge sources * Agentic workflows for handling multi-step legal queries * Clear explanations of complex legal terminology in plain language * Source citations and references for generated answers * Legal document upload, parsing, summarization, and question answering * Context-aware follow-up questions when user input is incomplete * Basic dispute-resolution and next-step guidance * User interaction logging and feedback collection * An evaluation mechanism for measuring answer relevance, faithfulness, and accuracy * A secure admin workflow for adding, updating, and managing legal knowledge ## AI and Machine Learning Requirements The AI system should: 1. Understand legal terminology and translate complex legal language into user-friendly explanations. 2. Use modern NLP and large language models to interpret user questions and generate contextually relevant responses. 3. Retrieve information from an approved legal database before producing legal answers. 4. Provide citations or references showing which legal sources were used. 5. Support multi-step reasoning and tool usage through an agentic architecture. 6. Improve over time through a controlled feedback and evaluation process. We do not expect the model to retrain automatically on every user conversation. Instead, the preferred approach is to capture feedback and interactions, evaluate response quality, improve prompts and retrieval, update the knowledge base, and optionally create curated datasets for future fine-tuning. ## Preferred Technical Approach We are open to architectural recommendations, but the preferred modern stack may include: * Python * FastAPI * OpenAI, Anthropic Claude, Gemini, or suitable open-source LLMs * LangGraph, LangChain, LlamaIndex, or a custom agent orchestration framework * PostgreSQL with pgvector, Pinecone, Weaviate, Qdrant, or another vector database * Hybrid semantic and keyword search * Reranking models for improved retrieval accuracy * Structured output and tool-calling workflows * OCR and document processing for PDF, DOCX, and scanned legal files * React or Next.js for the prototype interface * AWS, Azure, or Google Cloud * Docker and CI/CD * LangSmith, Arize Phoenix, RAGAS, DeepEval, or similar LLM observability and evaluation tools ## Key Responsibilities * Review the business concept and recommend the most suitable POC architecture * Design the Agentic AI and RAG workflow * Build ingestion pipelines for legal documents and structured legal data * Implement chunking, metadata extraction, embeddings, hybrid retrieval, and reranking * Develop a source-grounded conversational assistant * Implement document analysis and question-answering workflows * Add citations, confidence indicators, and fallback behavior * Create guardrails for unsupported, ambiguous, or high-risk queries * Build a basic web interface or integrate the AI backend with an existing platform * Implement user feedback and response evaluation mechanisms * Document the architecture, deployment process, and recommended roadmap for Version 2 ## Important Considerations Experience with the following will be highly valued: * Legal AI, LegalTech, RegTech, HRTech, or compliance platforms * Employment law knowledge systems * RAG accuracy and hallucination reduction * Knowledge graphs, legal taxonomies, and metadata-based retrieval * Multi-agent or agentic workflow architecture * LLM security, prompt injection protection, and access control * Personally identifiable information and confidential document handling * Human-in-the-loop review workflows * LLM evaluation and observability * Designing AI systems that provide information and guidance without making unsupported legal conclusions ## Expected Deliverables * Working Version 1 POC * Agentic AI and RAG backend * Legal knowledge ingestion pipeline * Searchable vector or hybrid knowledge database * Conversational legal assistant * Document upload and analysis workflow * Source citations in responses * Basic user interface or API integration * Feedback and evaluation workflow * Deployment to a cloud environment * Technical documentation * Recommendations, timeline, and cost estimate for production development ## Ideal Candidate You should have hands-on experience building production or prototype systems involving: * LLM applications * Agentic AI * RAG pipelines * Vector databases * Document processing * Prompt engineering * Tool calling and structured outputs * Backend API development * Cloud deployment * AI evaluation and monitoring Please include the following in your application: 1. Examples of relevant Agentic AI, RAG, LegalTech, or document intelligence projects 2. The technology stack you recommend for this POC 3. How you would reduce hallucinations and ensure answers are grounded in approved legal sources 4. How you would safely use user feedback to improve the system over time 5. Your estimated timeline and team composition 6. Any major technical or regulatory risks you would address during the POC Please begin your proposal with the phrase **“Legal AI POC”** so we know you have reviewed the full description.