Program Facilitator in RAG and Context Engineering for MIT xPRO. in Peterborough

Information about the Organization: Eruditus and its sister company Emeritus provide Private Online Courses (SPOCs) to students all over the world. We have partnered with top schools across the globe, such as Columbia Business School, The Tuck School of Business at Dartmouth, MIT, Wharton, London Business School, Cambridge, and UC Berkeley Haas School of Business, to provide condensed, intense, practical, and convenient programs, which can either be conducted completely or partially online. EMERITUS enables working professionals who cannot enroll into full-time courses to access a top-tier, affordable education that will give them the skills needed to be the business leaders of tomorrow. The Emeritus and Eruditus global team includes 1,000+ employees located in the US, Dubai, Mexico, India, China, and Singapore.

Information about the Learning Facilitator role: Emeritus is seeking a Program Facilitator in RAG and Context Engineering: Designing and Building Production-Grade AI Systems for MIT xPRO. The Program Facilitator should function as both a technical subject matter expert (SME) and a learning support partner capable of helping learners apply complex concepts in practice through the 8-week online program. The facilitator must have industry experience and demonstrated knowledge of the course subject area; they will guide learners through complex topics, requiring significantly more than general familiarity with AI tools. The Facilitator should have hands-on experience building or working with retrieval-aware AI systems and enough practical depth to support live labs, troubleshoot learner challenges, and guide architectural reasoning throughout the program. This is a great role for someone who wants work flexibility and the chance to diversify their resume with consultancy and teaching experience. This is an independent contractor position in a remote setting.

Key Responsibilities of the Program Facilitator include but are not limited to:

• Facilitate weekly office hours and moderate required discussion boards.
• Facilitate lab sessions weekly.
• Moderate and guide learner engagement during faculty live sessions.
• Support learners in synthesizing live session content delivered by the faculty (e.g., AI tools in cybercrime, GRC frameworks, deepfake detection).
• Assist with capstone assignment challenges.
• Collaborate with academic delivery managers to improve engagement and comprehension.
• Monitor learner progress and provide support or escalation as needed.

Qualifications for the role:

• 10+ years of extensive work experience as an Applied AI Consultant, Solutions Architect, Developer Educator, Technical Product Manager, Search/Retrieval Engineer or AI/ML Bootcamp Instructor.
• A bachelor’s degree is required; plus, a master’s degree is preferred in one of the following fields: engineering, artificial intelligence, machine learning or any related fields.
• Hands-on experience building or experimenting with Retrieval-Augmented Generation (RAG) systems.
• Experience working with LLM APIs (e.g., OpenAI, Anthropic, OpenRouter, Gemini).
• Experience implementing or testing AI-powered applications.
• Familiarity with vector databases, embeddings, semantic search, and retrieval workflows.
• Experience evaluating LLM outputs and diagnosing system failures.
• Experience supporting technical learners in applied or project-based environments.
• Solid experience with Large Language Models (LLMs) how they work conceptually, limitations, prompt engineering strategies, context windows and tokenization and trade-offs across different model providers.
• Solid experience with Retrieval-Augmented Generation (RAG), RAG architecture, retrieval pipelines, chunking strategies, embeddings, vector search, hybrid retrieval, reranking and retrieval quality trade-offs.
• Solid experience with Evaluation and System Diagnosis, evaluation frameworks, benchmarking approaches, qualitative and quantitative evaluation, retrieval metrics, output quality assessment, failure analysis, and iterative system improvement.
• Solid experience with Agentic and Tool-Based Systems, AI agents, orchestration concepts, tool calling, memory systems, retrieval-aware agents, and multi-step workflows.
• Strong knowledge with APIs and Technical Workflow Support, API-based workflows, Python notebooks/scripts, environment setup, debugging common technical issues, JSON structures, and reading technical documentation.
• Experience supporting executive or mid-career professionals in continuing education is highly preferred.
• Excellent communication, facilitation and interpersonal skills with an ability to listen effectively, respond appropriately, and maintain a mutual comfort level while working with a diverse population.
• Ability to translate technical content into actionable insights.
• Empathetic and learner-centered approach.
• Comfortable in an online environment, navigating LMS platforms (Canvas), Zoom, Slack, or similar environments.
• Strong organizational and time-management skills to manage weekly cycles of engagement and support.

Program Description and Context:

The RAG and Context Engineering: Designing and Building Production-Grade AI Systems program from MIT xPRO is an eight-week learning journey that equips participants to design, evaluate, and deploy retrieval-aware large language model (LLM) systems that perform reliably in real-world environments. In eight weeks, this program will provide participants with expertise in evaluation discipline, system auditing, retrieval-augmented generation (RAG) design, and the core principles that determine whether LLM systems succeed or fail in production.

Duration: 8 weeks (estimated learner commitment: 4–5 hours/week).

Structure: Blend of asynchronous Canvas activities and live Zoom sessions.

Target Audience: Mid- to senior-level professionals and technical leaders seeking to build end-to-end RAG and LLM systems.

Emeritus provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal or state laws.