AI Career Transition Webinar: what we will cover.

What You Will Walk Away With

• A clear understanding of where your current role fits in the AI transition path.
• A practical comparison of RAG, AI Agents, Document Intelligence, MLOps, and Full-Stack AI Engineering.
• A learning sequence for the next 90 days based on your current stack and experience level.
• Guidance on portfolio projects that prove real AI engineering capability.
• Answers to selected participant questions submitted before the webinar.

2-Hour Live Agenda

11:00-11:20 AM: AI Career Landscape

What is changing in software careers, why AI skills are becoming part of normal engineering work, and where developers, testers, support engineers, architects, and data professionals can enter.

11:20-11:45 AM: Understanding the AI Career Tracks

• Full-Stack AI Engineer / FDE-style path
• RAG and AI Agents path
• Document Intelligence path
• MLOps and AI platform engineering path
• AI architecture and technical leadership path

11:45 AM-12:25 PM: Live Career Question Breakdown

Selected participant questions will be grouped and answered based on current role, experience, stack, transition confusion, and target AI direction.

12:25-12:50 PM: Portfolio and Project Strategy

How to build proof around AI features, APIs, document workflows, agents, RAG systems, evaluation, deployment, and production readiness.

12:50-1:00 PM: Next Steps

A concise 90-day roadmap and course/project direction so participants know what to do immediately after the session.

Career Paths Covered

• For backend/full-stack developers: how to add RAG, agents, embeddings, APIs, and AI features to existing engineering skills.
• For QA and automation professionals: how to move toward AI-assisted testing, evaluation, automation, and AI product workflows.
• For support and operations engineers: how to use domain understanding as a bridge into AI-enabled business systems.
• For architects and tech leads: how to reason about AI systems, integrations, governance, reliability, and production readiness.
• For data professionals: how to connect data work with document AI, retrieval systems, and MLOps delivery.

Portfolio Project Ideas Discussed

• Document Q&A system using RAG, chunking, embeddings, and citations.
• AI agent workflow for business process automation.
• Document intelligence pipeline for extraction, validation, and structured output.
• MLOps pipeline with experiment tracking, model serving, monitoring, and deployment.
• Full-stack AI application with authentication, APIs, storage, deployment, and observability.

Suggested 90-Day Roadmap

Days 1-30: Foundation

Strengthen Python or your backend stack, API fundamentals, Git, cloud basics, prompt engineering, embeddings, and core AI terminology.

Days 31-60: Build

Create one focused project around RAG, Document Intelligence, Agents, or MLOps. Keep it small enough to finish but real enough to explain in an interview.

Days 61-90: Production Shape

Add authentication, logging, deployment, evaluation, monitoring, documentation, and a clear case-study write-up. This turns the project from a demo into portfolio proof.