Forward Deployed AI Engineer (Udemy)

Our take

Forward Deployed AI Engineer is a all level Forward Deployed AI Engineer course from Amit Prabhu on Udemy. It runs about 24 hours, and holds 4.4★ from 52 students in our Udemy course reviews.

Best for: all level learners who want one guided path through Forward Deployed AI Engineer. Skip it if: You already work professionally in Forward Deployed AI Engineer and need advanced architecture patterns.

📘 What you will learn

• Fundamental difference between Generative AI, Agentic AI, Machine Learning, and Deep Learning.
• Prompt Engineering in detail.
• How bias and hallucinations are introduced in the AI systems and step to detect, measure, and mitigate them.
• Key technical concepts and core capabilities of Traditional AI, Generative AI, and Agentic AI.
• LLM Model selection, Fine-tuning techniques and Retrieval-Augmented Generation (RAG).
• Data and AI Fundamentals and Data Annotation.
• Reference-based and Reference-free Inference Evaluation methods
• Natural Language Processing (NLP) Architectures such as RNN, LSTM, Encoder-only Transformers, Encoder-Decoder Transformers, Decoder-only Transformers.
• Agentic AI Architectures such as ReAct++, LLM-based Cognitive, Neuro-Symbolic, Swarm Agent, Embodied, and Reflexion.
• AI-enabled Security and LLM Adversarial attacks

📑 Course content

17 sections · 135 lectures · 24 hours of video · updated 10/2026

1. Getting started — 17 lectures · 3h
2. Core foundations — 19 lectures · 3.4h
3. Building real projects — 19 lectures · 3.3h
4. Intermediate techniques — 19 lectures · 3.4h
5. Deep dive & best practices — 20 lectures · 3.5h
6. Capstone project — 17 lectures · 3.1h
7. Next steps — 24 lectures · 4.3h

🎯 Who it is for

Suits: Complete beginners, career switchers, and self-taught devs who want structure.

Needs: Fundamental difference between Generative AI, Agentic AI, Machine Learning, and Deep Learning. Prompt Engineering in detail. How bias and hallucinations are introduced in the AI systems and step to detect, measure, and mitigate them. Key technical concepts and core capabilities of Traditional AI, Generative AI, and Agentic AI. LLM Model selection, Fine-tuning techniques and Retrieval-Augmented Generation (RAG). Data and AI Fundamentals and Data Annotation. Reference-based and Reference-free Inference Evaluation methods Natural Language Processing (NLP) Architectures such as RNN, LSTM, Encoder-only Transformers, Encoder-Decoder Transformers, Decoder-only Transformers. Agentic AI Architectures such as ReAct++, LLM-based Cognitive, Neuro-Symbolic, Swarm Agent, Embodied, and Reflexion. AI-enabled Security and LLM Adversarial attacks

👤 Instructor

Amit Prabhu — Amit Prabhu has over a decade of experience in global business management, strategy, and consulting. He holds a master’s degree in telecommunications and networking from the University of Pennsylvania. He is an author of business non-fiction: Digital Strategy Framework. He is also a business trainer, speaker, faculty, entrepreneur, and a swimming coach. Explorer and Teacher are the two words that describe him vividly. He lives in Stockholm, Sweden, with his wife and a son.

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