12-Month Advanced Diploma · AI Engineering
AI Engineering (Advanced)
40 hands-on modules · 24 tools & platforms · 320+ hours of training · An extended diploma covering AI engineering plus deployment, agents and production-grade workflows. Covers AI agents, fine-tuning basics, deployment and MLOps introduction, built around real model, agent and API work from day one.
Last updated: 27 August 2026
Quick answer
Skill Training Institute's AI Engineering (Advanced) diploma is a 12-month, 40-module, 320+ hour classroom and online program in Alpha-1, Greater Noida that takes you from ML and deep learning foundations through LLM engineering, RAG, fine-tuning (LoRA/QLoRA), multi-agent AI systems, and production deployment/MLOps. It is beginner-friendly (no prior AI experience required, basic Python familiarity helps), includes 6 portfolio projects and weekly practical assessments, and ends with 100% Placement Assist for roles such as AI/ML engineer, LLM application developer and AI agent developer.
Who this course is for
Built for graduates, developers and working professionals who want a deep, extended diploma in AI engineering — from machine learning foundations through LLMs, agents, fine-tuning and production deployment — no prior AI experience needed, every concept is taught from the ground up before advancing.
By the end you'll build and evaluate ML models, work fluently with LLM APIs and RAG pipelines, fine-tune open-source models, design multi-tool AI agents, and deploy a production-grade AI application with monitoring and cost controls, the way an AI/ML engineer would.
1Program structure
Four extended phases over 48 weeks — from ML and deep learning foundations, through LLM engineering and RAG, to fine-tuning and agents, to deployment and MLOps.
| Phase | Modules | Focus |
|---|---|---|
| Phase 1 — AI & ML Foundations for Engineers | 01–10 | Python, math for ML, core machine learning, data preprocessing and evaluation |
| Phase 2 — Deep Learning & LLM Engineering | 11–20 | Deep networks, transformers, LLM APIs, embeddings and RAG systems |
| Phase 3 — Fine-Tuning & AI Agents | 21–30 | Fine-tuning basics, LoRA/QLoRA, agentic workflows and responsible AI |
| Phase 4 — Deployment & MLOps | 31–40 | Containerization, CI/CD, monitoring, cloud deployment and production capstone |
2Full curriculum — all 40 modules
Click any phase to expand its modules, or open a module directly for a detailed description.
Phase 1AI & ML Foundations for EngineersModules 01–10Python, math for ML, core machine learning, data preprocessing and evaluation+
01Introduction to AI Engineering & the Industry Landscape+
Mapping the AI engineering role, the modern AI stack and where this diploma takes you.
02Python for AI Engineers: Core Refresher+
Refreshing the Python skills used every day for AI and ML engineering work.
03Mathematics for Machine Learning: Linear Algebra & Probability Essentials+
Building the linear algebra and probability intuition behind ML and neural network methods.
04Machine Learning Fundamentals: Supervised & Unsupervised Learning+
Training and comparing supervised and unsupervised models on real datasets.
05Data Preprocessing & Feature Engineering for ML+
Cleaning, transforming and engineering features that meaningfully improve model performance.
06Introduction to Neural Networks+
Understanding how neural networks learn, from perceptrons to backpropagation.
07Working with NumPy, Pandas & Scikit-learn+
Using the core Python data science stack to build and evaluate ML pipelines.
08Model Evaluation, Metrics & Cross-Validation+
Choosing the right metrics and validation strategy to judge a model honestly.
09Introduction to Deep Learning Frameworks (PyTorch/TensorFlow)+
Building and training your first neural network using a modern deep learning framework.
10Version Control & Experiment Tracking for ML Projects+
Managing code, data and experiments reproducibly using Git and experiment tracking tools.
Phase 2Deep Learning & LLM EngineeringModules 11–20Deep networks, transformers, LLM APIs, embeddings and RAG systems+
11Deep Neural Networks: Architecture & Training+
Designing deeper network architectures and diagnosing common training problems.
12Convolutional Neural Networks for Vision Tasks+
Applying CNNs to image classification and understanding how visual features are learned.
13Recurrent Networks & Sequence Modeling Basics+
Modeling sequential data with RNNs and understanding their limitations before transformers.
14Introduction to Transformers & Attention Mechanisms+
Understanding the attention mechanism that powers every modern large language model.
15Large Language Models: Architecture & Capabilities+
Exploring how LLMs are trained and what they can and cannot reliably do.
16Prompt Engineering for Production Applications+
Writing reliable, structured prompts for real applications rather than one-off experiments.
17Working with LLM APIs (OpenAI, Anthropic & Open-Source Models)+
Integrating leading commercial and open-source LLM APIs into working applications.
18Embeddings & Vector Representations+
Turning text into embeddings and using vector similarity for search and retrieval.
19Vector Databases & Semantic Search+
Storing and querying embeddings at scale using a production vector database.
20Retrieval-Augmented Generation (RAG) Systems+
Building a full RAG pipeline that grounds LLM answers in your own documents.
Phase 3Fine-Tuning & AI AgentsModules 21–30Fine-tuning basics, LoRA/QLoRA, agentic workflows and responsible AI+
21Fine-Tuning Basics: When and Why to Fine-Tune+
Deciding when fine-tuning beats prompting or RAG, and what it takes to do it well.
22Parameter-Efficient Fine-Tuning (LoRA, QLoRA)+
Fine-tuning an open-source model efficiently using LoRA and QLoRA techniques.
23Dataset Preparation for Fine-Tuning+
Curating and formatting a clean, task-specific dataset for fine-tuning a model.
24Evaluating & Benchmarking Fine-Tuned Models+
Measuring whether a fine-tuned model actually improved on the task that matters.
25Introduction to AI Agents & Agentic Workflows+
Understanding how agents plan, act and use tools beyond a single prompt-response turn.
26Building Tool-Using Agents+
Building an agent that can call external tools and APIs to complete real tasks.
27Multi-Agent Systems & Orchestration+
Coordinating multiple specialised agents to solve a task no single agent handles well.
28Agent Memory, Planning & Reasoning Patterns+
Adding memory and structured reasoning so agents handle longer, multi-step tasks reliably.
29Building Conversational AI Applications+
Designing a full conversational application on top of an LLM and agent backend.
30Responsible AI: Safety, Bias & Guardrails+
Adding safety checks, bias awareness and guardrails to AI systems before they ship.
Phase 4Deployment & MLOpsModules 31–40Containerization, CI/CD, monitoring, cloud deployment and production capstone+
31Introduction to MLOps & the ML Lifecycle+
Mapping the full lifecycle from experimentation to a production ML/AI system.
32Containerization for AI Applications with Docker+
Packaging an AI application and its dependencies into a portable Docker container.
33Model Serving & Inference APIs+
Exposing a trained model or agent as a reliable, callable inference API.
34CI/CD Pipelines for ML & AI Applications+
Automating testing and deployment of AI applications with a CI/CD pipeline.
35Model Monitoring, Logging & Observability+
Tracking model and agent behaviour in production, and catching drift or failures early.
36Scaling AI Applications: Caching, Batching & Load Balancing+
Handling real traffic with caching, request batching and load balancing strategies.
37Cost Optimization for LLM-Based Applications+
Controlling and reducing the real-world running cost of LLM-powered applications.
38Cloud Deployment for AI Workloads (AWS/GCP/Azure Basics)+
Deploying an AI application to a major cloud platform using core managed services.
39AI Engineering Career Roadmap & Portfolio Building+
Turning your projects into an interview-ready AI engineering portfolio and resume.
40Capstone: Building & Deploying a Production-Grade AI Agent Application+
Designing, fine-tuning, deploying and monitoring a complete AI agent application end-to-end.
324 tools & platforms you'll use
Everything is taught using the real frameworks, APIs and platforms used in production AI teams — not simulations. Swipe or use the arrows to browse every category.
4How each module is taught
Every module follows the same practical, build-it-yourself lens, so you always know what to expect in class.
Concept Walkthrough
Trainer explains the concept with a real AI product scenario first, then the underlying method or workflow.
Live Along
You build the same model, pipeline or agent, right alongside the trainer, in your own notebook or codebase.
Hands-on Exercise
An independent task applying the concept to a new dataset, prompt or deployment scenario.
Doubt Clearing
One-on-one help for anyone stuck, before the class moves to the next module.
Weekly Assessment
A short practical build or evaluation test every week so gaps are caught early, not at the end.
5Projects & portfolio
Six practical, portfolio-ready AI engineering projects across the full syllabus — the kind of work you'll show in interviews.
- Project 1. Train and evaluate a complete machine learning model end-to-end
- Project 2. Build a RAG-based Q&A application over a custom document set
- Project 3. Fine-tune an open-source LLM with LoRA for a specific task
- Project 4. Build a multi-tool AI agent that can plan and execute tasks
- Project 5. Containerize and deploy an AI application behind a production-grade API
- Project 6. Capstone: production AI agent system, deployed, monitored and cost-optimized
6Assessment & certification
Weekly practical assessments covering ML, LLM, agent and deployment workflows, with instructor review and feedback.
Final practical exam across all four phases, plus the capstone project used as your demonstrable, portfolio-ready proof of skill.
Advanced diploma certificate, plus resume and portfolio support through our 100% Placement Assist program.
7Key terms explained
Short, plain-language definitions of the core terms used throughout this diploma.
- Fine-Tuning
- Further training a pre-trained model on a smaller, task-specific dataset so it performs better on that particular task.
- LoRA / QLoRA
- Parameter-efficient fine-tuning techniques that adapt a large language model by training a small number of additional parameters instead of the whole model, reducing compute and memory needs.
- AI Agent
- A system built on an LLM that can plan, reason across multiple steps, and call external tools or APIs to complete a task rather than just answering a single prompt.
- MLOps
- The set of practices for deploying, monitoring and maintaining machine learning and AI models reliably in production, similar to DevOps for AI systems.
- Transformer / Attention Mechanism
- The neural network architecture and attention mechanism that underlies modern large language models, allowing them to weigh relationships between all parts of an input.
- Model Serving
- Exposing a trained model or agent as a callable API endpoint so applications can send requests and receive predictions or generated output.
8Quick answers
Do I need prior AI, ML or coding experience?
Basic Python familiarity helps, but every concept — from ML foundations to agents and deployment — is taught from the ground up across the 12 months.
Will I need to pay for LLM API usage during the course?
Labs are designed around free tiers and low-cost usage where possible, and Module 37 specifically covers controlling and reducing real LLM running costs.
Do I need a powerful laptop for the deep learning modules?
A standard laptop with a browser works, since heavier training and fine-tuning labs run on institute-provided or cloud compute rather than your local machine.
Will I work on real models and APIs, or is it mostly theory?
You'll build and ship real models, RAG pipelines and agents from week one. Every module includes hands-on practice, ending in a full production AI agent capstone.
Are classes online, offline, or both?
Both. Offline batches run at our Alpha-1, Greater Noida campus; online live batches follow the same curriculum with the same trainers. Morning, evening and weekend timings are available.
What can I do after completing this diploma?
You'll be ready for AI/ML engineer, LLM application developer and AI agent developer roles at startups and product teams, with a client-ready portfolio, and can progress into specialised MLOps or applied research tracks with the same placement support.
Ready to see a class live?
Book a free, no-obligation demo class and sit in on a real AI engineering session before you decide anything.