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Generative AI Engineering & AI Agents (AI/ML) — Professional Internship

12-Week Professional Track 240 Learning Slides Hands-On Engineering Practice Capstone & Portfolio Development
Level 2 · VELYON Learning Framework
13 WEEKS

Build Intelligent AI Applications. Engineer Autonomous Agents.

Master the engineering behind modern AI systems. Over 12 weeks, learn to integrate LLM APIs, build knowledge-grounded applications with RAG, engineer tool-enabled AI agents, orchestrate multi-step workflows, and evaluate your solutions through a practical capstone project.

Generative AI Engineering LLM Application Development AI Agent Engineering RAG & Knowledge Systems Python & API Integration AI Evaluation & Deployment
13 WEEKS
12 LIVE SESSIONS
3 REAL PROJECTS
1:1 MENTORSHIP
Portfolio ENGINEERING CASE STUDY

About This Program

Not Another AI Prompting Course. Learn to Engineer AI Systems.

Generative AI is changing how software is built, but knowing how to use an AI chatbot is different from knowing how to engineer an AI-powered application.The VELYON Generative AI Engineering & AI Agents Professional Internship Program is designed for learners who want to move beyond basic prompting and understand how real AI applications are designed, developed, evaluated, and prepared for deployment.Across 12 structured chapters, you will explore the engineering principles behind Large Language Model (LLM) applications, Python-based API integration, data preparation, Retrieval-Augmented Generation (RAG), tool-enabled AI agents, multi-step workflows, agent memory, security, and production monitoring.Instead of studying these topics as isolated concepts, you will learn how they connect within a complete AI engineering lifecycle.What makes this program different?
  • 12 Chapters, 240 Learning Slides: A structured curriculum that progresses from application architecture to a complete engineering capstone.
  • Engineering-Focused Learning: Understand system design, data flow, integrations, execution boundaries, and architectural trade-offs.
  • Practical Implementation: Apply concepts to AI applications, knowledge retrieval, tool calling, workflow orchestration, and evaluation.
  • Reliability and Security: Learn to identify hallucinations, test system behaviour, manage failures, protect sensitive information, and control agent actions.
  • Evaluation Before Deployment: Understand how to measure correctness, retrieval quality, latency, operational cost, and reliability.
  • Portfolio-Oriented Capstone: Bring the concepts together in a documented AI application or agent prototype that demonstrates your engineering approach.
Your learning journeyLearn → Design → Implement → Evaluate → DeployBy the end of the program, your objective is not simply to demonstrate that an AI model can produce an answer. It is to explain how an AI system works, how its components interact, how its failures are handled, and how its quality can be measured and improved.If you want to develop practical AI engineering skills and build a stronger technical portfolio, this program provides a structured path to begin that journey.

Meet Your Mentors

Nitesh Roy

Co-Founder & Technology Mentor at VELYON. Brings experience in web development, AI-powered products, and technology-led business development. Focuses on helping learners understand practical implementation, system architecture, and the process of turning technical concepts into useful applications.

What You'll Build

  • A Python-based LLM application with structured outputs and error handling
  • A document-processing pipeline for preparing clean, searchable knowledge
  • A RAG-powered question-answering application with source references
  • A tool-enabled AI agent that executes controlled tasks

  • A multi-step AI workflow with task dependencies and error recovery
  • An AI application evaluation dataset and testing report
  • An end-to-end capstone prototype with documented architecture
  • A portfolio-ready technical case study and project demonstration

What You'll Master

  • LLM application architecture and system design
  • Advanced prompt engineering and structured AI outputs
  • Python integration with LLM APIs
  • Secure API authentication and error handling
  • Data ingestion, cleaning, preprocessing, and document chunking
  • Embeddings, vector search, hybrid retrieval, and RAG evaluation

  • AI agent architecture, tool calling, and execution control
  • Multi-step workflows, orchestration, and task-state management
  • Agent memory, context engineering, and persistent state
  • Prompt injection awareness, guardrails, and least-privilege access
  • Evaluation datasets, regression testing, observability, and deployment planning
  • Performance optimisation, reliability analysis, and operational cost awareness

Cohort Timeline

Month 1 — AI Application Engineering

Weeks 1–2

AI engineering architecture, LLM application design, advanced prompt engineering, and structured AI outputs.

Weeks 3–4

LLM APIs, Python integration, application development, data ingestion, document processing, and data quality.

Month 2 — RAG & AI Agent Engineering

Weeks 5–6

RAG architecture, embeddings, vector search, hybrid retrieval, retrieval evaluation, and knowledge quality optimisation.

Weeks 7–8

AI agent architecture, tool calling, controlled execution, task decomposition, and multi-step workflow orchestration.

Month 3 — Production Readiness & Capstone

Weeks 9–10

Agent memory, state management, context engineering, security guardrails, permissions, and controlled execution.

Weeks 11–12

AI evaluation, observability, deployment planning, end-to-end AI agent capstone, and engineering portfolio documentation.

You Won't Finish With Just a Certificate

Don't finish with AI concepts alone. Work toward a complete AI engineering prototype, supported by technical documentation, evaluation evidence, identified limitations, and a portfolio-ready case study.

Earn Your Generative AI Engineering & AI Agents (AI/ML) — Professional Internship Certificate

Complete the required internship projects and evaluation to earn your VELYON Professional Certificate — portfolio-ready, internship-experience-ready, LinkedIn-ready.

Joining Letter Sample Joining Letter
Certificate Sample Certificate

Fellowship — Stage 3

Live business projects and direct placement support, open to top performers.

Learn About Fellowship

Build Your AI Engineering Skills Through a Structured 12-Week Professional Track.

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  • Professional / Intermediate
  • 12 Weeks | Structured Curriculum
  • 12 Chapters | 240 Learning Slides
  • LLM Applications | RAG | AI Agents
  • Practical Engineering & Evaluation
  • End-to-End Capstone Project
  • Technical Documentation & Portfolio Development

Material Includes

  • 12 Chapter-Wise Learning Presentations
  • 240 Structured Learning Slides
  • AI Engineering Architecture & Workflow Examples
  • LLM API Integration and Python Implementation Exercises
  • RAG and Knowledge Retrieval Learning Materials
  • AI Agent and Tool-Calling Workflow Exercises
  • Evaluation, Security, and Reliability Checklists
  • End-to-End Capstone Project Framework
  • Technical Documentation and Portfolio Case Study Guidance

Requirements

  • Basic familiarity with programming concepts
  • Prior exposure to Python is recommended
  • Understanding of APIs and basic web application concepts is helpful
  • A laptop or desktop computer with a stable internet connection
  • Willingness to write, test, debug, and improve code
  • Readiness to work through technical examples and implementation exercises
  • Interest in Generative AI, LLM applications, and AI agent development
  • Commitment to completing the capstone and documenting project results

Tags

Generative AI Engineering AI Agent Engineering LLM Application Development Python & API Integration Retrieval-Augmented Generation Professional Internship AI Capstone Project Engineering Portfolio
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