AI Frontier Engineering
in AI & LLMsAbout this course
Engineer the AI Systems Shaping What's Next
ProDAC's AI Frontier Engineering program takes you from machine learning and deep learning through transformers, RAG, LangChain and agentic AI in 120 hours of live, mentor-led learning, built for people who want to build real products powered by modern AI, not just experiment with a chatbot API.
Built for Career Outcomes, Not Just Course Completion
Every module, project and mentoring session is designed around one goal: making you genuinely employable as an AI engineer.
Industry-Aligned AI Curriculum
From classical ML to transformers, RAG and agentic AI, the same stack modern AI product teams actually build with.
Real Models, Real Applications
Build with actual LLMs and real retrieval pipelines, not toy prompts, so what you learn survives outside a demo.
From Classical ML to Agentic AI
A deliberate progression: ML and deep learning first, then transformers and LLMs, then retrieval, then autonomous agents.
A Portfolio You Can Show
Leave with a portfolio of real-world AI projects and a capstone agent you can walk an interviewer through, step by step.
Structured Interview Prep
Practice explaining model and architecture choices, from fine-tuning tradeoffs to agent design, before a real interview.
Learn-by-Doing Format
Every concept, from a neural network to an agent orchestration graph, is paired with a hands-on build.
Where This Program Can Take You
AI engineering skills are in demand across tech, BFSI, healthcare, retail and consulting: anywhere teams are building products powered by modern AI.
Indicative Salary Progression (India)
Indicative industry ranges, not a guarantee. Actuals vary by city, company and experience.
As companies race to build products on top of large language models, demand is growing fast for engineers who understand both the machine learning fundamentals underneath and how to build reliable, production-grade AI applications on top of them.
The AI Stack You'll Master
A focused, in-demand stack: real tools, real colors, hover to see the name.
Your 120-Hour Journey, Mapped Out
A deliberate progression from ML fundamentals to building with LLMs and autonomous agents, about 14 to 15 weeks at a steady, structured pace.
Foundation
Career prep, Python & OOP
Data Handling
NumPy, Pandas & EDA
Machine Learning
Core ML algorithms
Deep Learning & NLP
Neural networks & language
LLMs & GenAI
Transformers, RAG & LangChain
Agentic AI
Orchestration & capstone
120 Hours, 15 Modules, Zero Filler
Tap any module to see exactly what you'll learn, build and submit.
Topics
- Resume building & LinkedIn setup
- GitHub basics & version control
- SDLC & Agile methodology
- Understanding the AI engineering ecosystem
Practical Exercises
- Create and push your first GitHub repository
- Set up a baseline AI engineering resume
Assignments
- Publish a GitHub profile README
- Complete a self-assessment of current skill gaps
Topics
- Variables, data types & control flow
- Functions & modules
- Lists, dictionaries, tuples & sets
- File handling & error handling
Practical Exercises
- Build a data-cleaning script for a messy dataset
- Write reusable functions for common data tasks
Assignments
- Solve 20+ Python programming problems
- Build a simple command-line data utility
Topics
- Classes & objects
- Inheritance & encapsulation
- Polymorphism
- OOP design for AI pipelines
Practical Exercises
- Model a simple ML pipeline using classes
Assignments
- Build a small OOP-based data validation tool
Topics
- NumPy arrays & vectorized operations
- Pandas Series & DataFrames
- Data cleaning & transformation
- Merging, joining & groupby aggregation
Practical Exercises
- Clean and transform a multi-source dataset
Assignments
- Build an automated data-preparation pipeline
Topics
- Descriptive statistics
- Univariate & bivariate analysis
- Outlier & correlation analysis
- Visualization for modeling decisions
Practical Exercises
- Perform EDA ahead of a modeling task
Assignments
- Submit an EDA report identifying features worth modeling
Topics
- Linear & logistic regression
- Decision trees, random forests & boosting
- Clustering & dimensionality reduction
- Model evaluation, tuning & cross-validation
Practical Exercises
- Build and compare 5+ classification and regression models
Assignments
- Train, tune and evaluate a model on a real business dataset
Topics
- Artificial Neural Networks (ANN) fundamentals
- Convolutional Neural Networks (CNN) for images
- Recurrent Neural Networks (RNN) for sequences
- Activation functions, backpropagation & optimizers
Practical Exercises
- Build and train a neural network from scratch
- Train an image classifier with a CNN
Assignments
- Design and tune a deep learning model for a chosen problem
Topics
- Tokenization & stemming
- Bag-of-words & TF-IDF
- Text classification & sentiment analysis
- From classical NLP to language models
Practical Exercises
- Build a text classification pipeline end to end
Assignments
- Train a sentiment analysis model on real review data
Topics
- Self-attention & the transformer architecture
- Encoder-decoder vs. decoder-only models
- How modern LLMs are actually built
- Using pretrained transformer models
Practical Exercises
- Use a pretrained transformer for a real text task
Assignments
- Compare outputs across different pretrained transformer models
Topics
- Prompt design patterns
- Few-shot & chain-of-thought prompting
- Structured output & function calling
- Evaluating prompt reliability
Practical Exercises
- Design and test prompts for a real task across multiple approaches
Assignments
- Build a prompt library for a chosen application
Topics
- Why RAG, and when to use it
- Chunking & embedding documents
- Retrieval strategies
- Grounding LLM answers in real data
Practical Exercises
- Build a RAG pipeline over a real document set
Assignments
- Build a Q&A system grounded in a chosen knowledge base
Topics
- Embeddings & similarity search
- Vector database fundamentals
- Graph database concepts for relationships
- Choosing VDB vs. GDB for a use case
Practical Exercises
- Set up and query a vector database for a RAG pipeline
Assignments
- Benchmark retrieval quality across different embedding setups
Topics
- Chains, tools & memory in LangChain
- Building multi-step LLM workflows
- LangGraph for stateful, graph-based flows
- Debugging complex chains
Practical Exercises
- Build a multi-step LLM workflow using LangChain
Assignments
- Build a stateful workflow using LangGraph
Topics
- When to fine-tune vs. prompt or use RAG
- Parameter-efficient fine-tuning (LoRA basics)
- Preparing fine-tuning datasets
- Evaluating a fine-tuned model
Practical Exercises
- Fine-tune a small model on a custom dataset
Assignments
- Fine-tune and evaluate a model against a baseline prompt approach
Topics
- Agent architectures & planning loops
- Tool use & function calling
- Multi-agent orchestration patterns
- Guardrails & reliability for autonomous agents
Practical Exercises
- Build an agent that plans and calls tools to complete a task
Assignments
- Design and build a small multi-step autonomous agent
The Agentic AI Capstone
One end-to-end build that mirrors a real AI engineer's project: ground an LLM in real data with RAG, compose the workflow with LangChain or LangGraph, give it tools it can call, and orchestrate it into an autonomous agent, then present the architecture and design decisions to a mentor panel, just like a real product review.
This is the project you'll lead with in interviews.
Build a RAG pipeline that grounds responses in real data.
Wire the workflow together with LangChain or LangGraph.
Give it tools and turn it into a planning, acting agent.
Walk a mentor panel through your architecture and tradeoffs.
From Last Module to First Offer
Skill-building is half the journey. The other half is making sure you can sell those skills.
Resume Building
Craft a results-driven, AI-engineering-ready resume that survives the first filter.
LinkedIn Optimization
Build a profile that recruiters actually find and message.
Mock Interviews
Practice live with technical, architecture and case-based interview rounds.
Portfolio Reviews
Get mentor feedback on every model, agent and project before you ship it.
Career Mentoring
1:1 guidance on roles, companies and negotiation.
Aptitude & Stats Training
Sharpen the statistics and reasoning fundamentals AI interviews test first.
Built for Every Starting Point
Hover over each path to see who it's built for. Whichever stage you're starting from, the curriculum meets you there.
Students & Freshers
Build job-ready AI engineering skills, especially useful if you come from a math, stats or CS background.
Working Professionals
Move from analytics or data science into AI and LLM engineering with weekend and evening batches that fit your job.
Career Switchers
Move into AI engineering from a non-tech background with a structured, mentor-guided path.
Software Developers
Already write code? Add LLMs, RAG and agentic AI to a strong existing engineering base.
AI Frontier Engineering Program
Verify at prodac.iokode.com
A Certificate That Verifies Real Skill
Your ProDAC Certified AI Frontier Engineer certificate is a digitally verifiable record of what you actually built.
Each certificate carries a unique ID that can be validated on ProDAC's verification page.
Add it directly to your profile to signal verified, project-backed skills to recruiters.
Comes with real-world AI projects and a capstone agent as proof, alongside the certificate.
Ready to Accelerate Your Career?
Talk to our admissions team and find out if this program is the right next step for you.
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