Description
Summary Overview We are seeking a skilled AI Engineer / Developer to architect, build, and deploy an enterprise-grade conversational AI platform. You will be responsible for creating a context-aware AI agent ecosystem that handles automated customer interactions, queries our internal knowledge bases, executes business workflows via API integrations, and hands off seamlessly to human operators. If you have hands-on experience building production-level Retrieval-Augmented Generation (RAG) systems, multi-agent frameworks, and deep platform integrations, we would love to collaborate with you. Key Responsibilities Architecture & Development: Design and build production-ready conversational AI agents using state-of-the-art LLMs (OpenAI, Claude, Gemini, Llama). RAG Pipelines: Implement end-to-end RAG workflows utilizing advanced vector database architectures and context management strategies. Agentic Workflows: Construct autonomous AI agents capable of multi-step task execution, tool usage, and dynamic API orchestration. System Integrations: Seamlessly connect conversational interfaces to existing infrastructure (CRMs, ERPs, web platforms, and custom backend services). Optimization & Security: Refine prompt engineering, enforce robust memory management, and ensure data privacy, speed, and low-latency responses. Admin & Analytics: Build administrative controls and monitoring tools for tracking conversation metrics, user queries, and performance analytics. Technical Requirements (Must-Have) Languages & Backend: Python (FastAPI, Flask, or Django), REST APIs. AI & Agent Frameworks: LangChain, LangGraph, or custom agentic execution frameworks. LLM Expertise: API integration & fine-tuning with OpenAI, Anthropic Claude, Google Gemini, or open-source models (Llama). Vector Databases: Pinecone, Weaviate, ChromaDB, Milvus, or FAISS. Data & Caching: PostgreSQL / MongoDB, Redis. DevOps & Cloud: Docker, AWS / Azure / GCP, Git / GitHub workflows. Preferred Experience (Nice-to-Have) Model Context Protocol (MCP) & Function Calling / Tool Use execution. Multi-agent communication and orchestration frameworks. Messaging channel integrations (WhatsApp Business API, Twilio, Slack, Teams, Discord). Voice AI / STT / TTS pipelines. Document parsing & OCR workflows (unstructured data processing). Enterprise security standards (OAuth2, JWT, SSO, RBAC). Container orchestration (Kubernetes) and CI/CD deployment pipelines. Project Scope & Capabilities The AI platform will support: Conversational natural language understanding with document analysis (PDF, Docs). Knowledge base auto-search & instant FAQ handling. Automated lead qualification, scheduling, and direct CRM synchronization. Multi-language support and graceful human agent handoff. User authentication and role-based access control (RBAC). Comprehensive analytics logging and email/event notifications. Key Deliverables Fully functional, production-ready AI application. Clean, well-documented source code and API specs. Automated deployment scripts and container configurations. Admin control panel & analytics dashboard. Ingestion pipeline for dynamic knowledge base updates. Documentation, user guides, and post-launch support/QA. How to Apply To apply, please provide: A brief overview of your background in AI and software engineering. Portfolios or links to live AI agent / RAG / Chatbot applications you have built. Your core technology stack and favorite LLM development frameworks. An estimated timeline and cost structure (hourly rate or fixed project bid) for an application of this scope. Your timezone and meeting availability.