Artificial Intelligence Engineer

Build and ship AI features, not just prompt a chatbot.

ModeOnline · Offline · Hybrid

FormatLive, project-based

IncludesCapstone + Portfolio

SupportInterview calls till placement

Enquire Now ← All Programs

Overview

Most 'AI courses' stop at prompting. This one goes further — covering how AI features are actually built and shipped: working with LLM APIs, retrieval-augmented generation, fine-tuning basics, and the evaluation discipline that separates a demo from a production feature.

Tools & Technologies

PythonOpenAI/Anthropic APIsLangChainVector databasesHugging FacePrompt engineering frameworksGit

Curriculum

AI & ML Foundations

How modern AI models actually work
Python for AI development
Working with pretrained models
Hugging Face ecosystem

Working with LLMs

API integration
Prompt engineering & evaluation
Retrieval-augmented generation (RAG)
Fine-tuning vs prompting — when to use which

Building AI Features

Vector databases & embeddings
Building an AI-powered app
Cost, latency & reliability tradeoffs
Responsible AI & guardrails basics

Capstone Project

End-to-end AI feature build
Evaluation & testing
Deployment basics
Portfolio presentation

Who This Is For

💻

Developers

Want to add AI features to their existing skillset.

🎓

CS/Engineering graduates

Aiming directly for AI-focused roles.

🔄

Tech professionals

Upskilling to stay relevant as AI reshapes most tech roles.

Roles You Can Target

AI EngineerML EngineerAI Application DeveloperPrompt/AI Solutions Engineer