AI Engineer Tutor & Mentor (Python, FastAPI, Machine Learning, RAG, AI Agents, AWS)

We are looking for an experienced AI Engineer to provide structured, hands-on tutoring and mentorship for an end-to-end enterprise AI engineering learning path. The ideal candidate has real-world experience building and deploying production AI applications and can explain complex concepts clearly through practical examples. Learning Objective The tutoring program will focus on building a single enterprise-scale AI application that evolves through multiple phases, allowing each new technology to be integrated into the same project rather than creating unrelated demos. Phase 1 – Enterprise Backend Foundation Python FastAPI React integration SQL & PostgreSQL Authentication and authorization REST APIs Users, organizations, roles, documents, and CRUD operations Phase 2 – Machine Learning Services Data preprocessing NumPy Pandas scikit-learn Model training and evaluation Prediction APIs with FastAPI Classification, prediction, and recommendation features Phase 3 – Generative AI & RAG LLM APIs Prompt engineering Embeddings pgvector Retrieval-Augmented Generation (RAG) Enterprise document chat with citations Streaming AI responses Phase 4 – AI Agents & MCP AI agents Tool calling Model Context Protocol (MCP) Workflow orchestration Enterprise integrations AI-powered workflow automation Phase 5 – Cloud & Production AI Docker AWS CI/CD Kubernetes MLOps Monitoring Security and production deployment Session Format Two sessions per week Two hours per session (4 hours per week total) Each week should follow this structure: Approximately 1 hour of focused instruction covering concepts, architecture, and best practices. Approximately 3 hours of hands-on implementation, building and improving the enterprise project. A small homework assignment to reinforce learning before the next session. What We’re Looking For Strong professional experience with Python and FastAPI Experience designing and deploying enterprise AI applications Strong understanding of PostgreSQL and API architecture Experience with machine learning fundamentals and model deployment Hands-on experience with RAG, embeddings, and vector databases Experience building AI agents and working with MCP AWS deployment experience, including Docker, CI/CD, and Kubernetes Ability to explain concepts clearly and adapt lessons to different skill levels Comfortable reviewing code, answering questions, and providing practical guidance Preferred Experience Production experience with OpenAI, Anthropic, Amazon Bedrock, or similar LLM platforms Experience with LangChain, LlamaIndex, or LangGraph Familiarity with MLflow, observability, and production AI monitoring Experience mentoring developers or teaching technical subjects The goal is to deliver a structured, enterprise-focused learning experience that combines architecture, best practices, and extensive hands-on development to build a production-ready AI platform from the ground up.