Lead Instructor (Part Time) - AI Solutions Engineering Program

Employment Type: Part-time
Work Mode: Fully remote (live online classes)
Program Duration: 12 weeks

About the Role
Edversity is looking for a part-time Lead Instructor to lead its Forward Deployed AI Engineer program. Forward Deployed AI Engineers work directly with clients to understand their business problems. They then design, build, and deploy AI solutions (LLM applications, agents, and automation) inside real customer environments.
The role suits experienced AI practitioners who want to teach remotely alongside their existing professional work. As Lead Instructor, you will guide the program’s academic delivery and technical direction. You will work with teaching assistants and prepare students for client-facing AI engineering roles.

Key Responsibilities
Program Leadership

• Shape and maintain the program curriculum, including course structure, learning outcomes, projects, and assessments.
• Guide and support teaching assistants so teaching quality stays consistent.
• Set grading criteria and project evaluation rubrics.
• Coordinate with the Edversity team to keep the program aligned with employer needs and hiring trends.
• Share periodic updates on student performance and suggested program improvements.

Teaching & Mentorship

• Deliver live online lectures, labs, and workshops on core and advanced topics in AI engineering and deployment during scheduled class days.
• Teach practical LLM application development, including prompt engineering, retrieval-augmented generation (RAG), AI agents, tool use, fine-tuning basics, and model evaluation.
• Run client-simulation exercises that cover requirement discovery, solution scoping, prototyping, and presenting to stakeholders.
• Supervise capstone projects in which students build and deploy production-ready AI solutions.
• Review student code and architecture, and give structured, actionable feedback.
• Coach students on the consulting, communication, and documentation skills that client-facing roles require.

Continuous Improvement

• Keep course content current with developments in AI models, frameworks, and deployment practices.
• Contribute reusable teaching materials, lab environments, starter templates, and project datasets.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, AI, Data Science, or a related field.
• 3+ years of industry experience in AI/ML engineering, software engineering, solutions engineering, or forward deployed engineering, including hands-on work shipping AI or LLM-based products.
• 1+ years of teaching, corporate training, or team leadership experience.
• Strong proficiency in Python and working knowledge of JavaScript/TypeScript.
• Proven experience deploying AI applications to cloud environments and integrating them with client systems.
• Excellent communication and presentation skills in English and Urdu.
• Availability to teach live online sessions on scheduled class days, with a reliable internet connection and a suitable setup for remote teaching.

Tools & Technologies

• Programming & Frameworks: Python, JavaScript/TypeScript, SQL, FastAPI, Flask, Node.js, Next.js/React (for demos and front-ends), Streamlit or Gradio (for rapid prototypes).
• LLMs & Model Providers: OpenAI, Anthropic (Claude), Google Gemini, Meta Llama, Mistral; running open-source models with Hugging Face, Ollama, or vLLM.
• AI Engineering Frameworks: LangChain, LangGraph, LlamaIndex, CrewAI or similar agent frameworks, Model Context Protocol (MCP), function/tool calling.
• Retrieval & Vector Databases: Pinecone, Chroma, Weaviate, Qdrant, pgvector; embedding models and document parsing tools.
• Evaluation & Observability: LangSmith, Langfuse, Weights & Biases, Ragas or similar evaluation tools; logging and monitoring for AI systems.
• ML & Data: Pandas, NumPy, scikit-learn, PyTorch (fine-tuning basics), Jupyter Notebooks, PostgreSQL, MongoDB, basic ETL pipelines.
• Cloud & Deployment: AWS (Bedrock, SageMaker, Lambda), Microsoft Azure (Azure OpenAI), or Google Cloud (Vertex AI); Docker; basic Kubernetes; CI/CD with GitHub Actions or GitLab CI.
• APIs & Integration: REST and GraphQL APIs, Postman, webhooks, OAuth; integrating AI into platforms such as Slack, Salesforce, HubSpot, or ERP systems; workflow automation with n8n, Zapier, or Make.
• Development & Collaboration: Git, GitHub/GitLab, VS Code or Cursor, AI coding assistants (Claude Code, GitHub Copilot), Linux command line, Jira or Trello, Notion or Confluence.
• Teaching Tools: Zoom, Google Meet or Microsoft Teams for live sessions; Google Classroom or an LMS; Google Workspace / Microsoft Office; Miro or Excalidraw for virtual whiteboarding; session recording tools.

Preferred Skills

• Prior experience in a forward deployed, solutions engineering, or AI consulting role with direct client exposure.
• Cloud or AI certifications (AWS, Azure, GCP).
• Understanding of AI safety, data privacy, security, and responsible AI practices in client deployments.
• Experience designing bootcamp-style or project-based technical programs.