D24
Flagship ProgramHigh DemandData & AIAdvanced10 weeks
AI Engineering: Generative AI & Agentic AI
AI Engineering is a 10-week advanced course connecting neural network fundamentals, Transformers, and Hugging Face to production skills: prompt engineering, RAG with vector databases, agentic AI frameworks, LLM evaluation, and containerized deployment. Built for developers who already know ML/DL basics and want to ship real generative and agentic AI applications, not just understand the theory.
Go from neural network fundamentals to building and deploying production AI applications — Transformers, RAG, vector databases, and multi-agent systems, built and shipped, not just studied.
What you'll learn
Transformer architecture, embeddings, and vector databases
Retrieval-augmented generation (RAG) and its common variations
Agentic AI: tools, memory, and multi-agent orchestration patterns
LLM evaluation, observability, and containerized deployment
Course details
Duration
10 weeks
Best for
Developers who already understand machine learning and deep learning fundamentals
Enrollment isn't open for this course yet — check back soon or contact us.
Course Curriculum
A module-by-module breakdown of what you'll cover in AI Engineering: Generative AI & Agentic AI.
01Part 1: AI Engineering and Software Foundations
3 topicsWeek 1
- AI Engineering Roles and Lifecycle
- Python Engineering
- APIs Git and Containers
02Part 2: Machine Learning for AI Engineers
3 topicsWeek 2
- End to End Machine Learning
- Evaluation and Model Selection
- ML Service Integration
03Part 3: Deep Learning Foundations
3 topicsWeek 3
- Neural Networks and Optimisation
- Framework and Accelerator Workflow
- Modern Neural Architectures
04Part 4: Natural Language Processing Transformers and LLMs
3 topicsWeek 4
- NLP and Embeddings
- Transformers and Large Language Models
- Provider and Model Integration
05Part 5: Prompt Engineering Structured Outputs and Adaptation
3 topicsWeek 4
- Prompt Design
- Structured Generation and Validation
- Fine Tuning and Model Adaptation
06Part 6: Retrieval Augmented Generation and Knowledge Systems
4 topicsWeek 5
- Document Ingestion and Indexing
- Retrieval and Reranking
- RAG Generation and Evaluation
- Advanced Knowledge Architectures
07Part 7: Agents Tools and Workflow Automation
4 topicsWeek 6
- Tool Use and Function Calling
- Agent Architecture and State
- Agent Reliability and Evaluation
- Automation and Business Integration
08Part 8: Multimodal AI
3 topicsWeek 7
- Vision and Vision Language Models
- Speech Audio and Video
- Multimodal Product Integration
09Part 9: AI APIs Product Engineering and User Experience
4 topicsWeek 8
- AI Backend Architecture
- AI Interfaces and Product UX
- Data Privacy and Application Security
- Feedback Analytics and Product Improvement
10Part 10: Deployment Inference and Cloud Architecture
4 topicsWeek 9
- Model Serving and Inference
- Cloud and Kubernetes Architecture
- Scaling Reliability and Cost
- CI CD and Infrastructure Automation
11Part 11: Evaluation LLMOps Monitoring and Responsible AI
4 topicsWeek 9
- AI Evaluation Engineering
- Experiment Tracking and AI Asset Management
- Observability and Monitoring
- Responsible AI Governance and Red Teaming
12AI Pro
3 topicsWeek 10
- Using Claude, ChatGPT, and Perplexity to design and debug prompts and agent behavior
- Verifying AI-generated code, prompts, and agent outputs before trusting them in production
- Project: An end-to-end AI application — from prompt design through RAG or agent logic to a deployed, containerized service
Tools & Technologies
Hands-on practice with the same tools used by working professionals.
PyTPyTorch
HugHugging Face
LanLangChain
OllOllama
CreCrewAI
Docker
ClaClaude / ChatGPT
Need this built for your business instead?
If you'd rather have it done than learn it, our solutions team delivers this work for businesses directly.
Real-Time, Industry-Based Projects
This is project-based learning modeled on real industry scenarios — you'll build portfolio-ready work as you go, not just watch lectures.
A fine-tuned model adapted to a custom domain using transfer learning
A retrieval-augmented chatbot answering questions grounded in a real document set
A multi-agent system where specialized agents collaborate on a research or verification task
A containerized, deployed AI application with basic evaluation and observability in place
Every course includes a guided internship on top of your project work — so you graduate with real experience, not just a certificate.
Communication & Soft Skills
Built into every course, alongside the technical curriculum.
Communication Skills
Practice explaining technical work clearly to teammates, managers, and clients.
Practical AI Skills
Hands-on training in using AI tools well — prompting, everyday workflows, and where AI actually helps versus where it doesn't.
Teamwork & Collaboration
Work in small project teams using real workflows like stand-ups and code reviews.
Problem Solving
Build the habit of breaking down ambiguous problems into clear, solvable steps.
Time Management
Learn to plan, prioritize, and deliver project work against realistic deadlines.
Presentation Skills
Present your project work and results with confidence, as you would to a client.
Resume & Profile Building
Build a strong resume, LinkedIn profile, and project portfolio that recruiters notice.
Interview Preparation
Mock interviews and portfolio reviews to get you ready for real job applications.
Career & Placement Support
100% of students enrolled in AI Engineering: Generative AI & Agentic AI get full access to placement support — real tools and guidance, not just a promise. This is support and access, not a guaranteed job outcome.
Resume Builder
A resume auto-built from your real completed projects and certificates, ready to export.
Shareable Portfolio
A public portfolio page showcasing your approved projects, with a link you can share with employers.
Interview Preparation
Guidance on presenting your projects and experience clearly in real interviews.
Peer & Mentor Community
Ongoing access to our learner community for questions, feedback, and support after you finish.
What Your Certificates Look Like
Real, verifiable certificates with your name, AI Engineering: Generative AI & Agentic AI, and a QR code anyone can scan to confirm they're genuine — one for completing the course, and a separate one if you take the internship track.
Course Completion Certificate
SAMPLE
Data24ZoneCertificate of Completion
Illustrative recipient
Your Name Here
Example certificate for completion of
AI Engineering: Generative AI & Agentic AI
- Certificate No.
- D24-SAMPLE
- Mode
- Self-Paced
- Duration
- 10 weeks
- Example issue date
- On completion
Sample only — not a real, issued certificate.
Verification is available only on issued certificates.
Internship Completion Certificate
SAMPLE
Data24ZoneCertificate of Internship
Illustrative recipient
Your Name Here
Example certificate for completion of
AI Engineering: Generative AI & Agentic AI
- Certificate No.
- D24-SAMPLE
- Mode
- Internship
- Duration
- 10 weeks
- Example issue date
- On completion
Sample only — not a real, issued certificate.
Verification is available only on issued certificates.
Frequently Asked Questions
What does an AI engineer do?
An AI engineer builds software products on top of machine learning and large language models — integrating model APIs, designing prompts and structured outputs, building retrieval (RAG) systems and agents, and deploying, evaluating, and monitoring them in production.
What is the difference between AI engineering and data science?
Data science focuses on analysing data and building models. AI engineering focuses on turning models — especially LLMs — into reliable applications: APIs, RAG pipelines, agents, deployment, cost control, and evaluation. The AI Engineering program is built around that application-building path.
Do I need to know Python for AI engineering?
Yes, Python is the main language. The program opens with an AI Engineering and Software Foundations part covering Python engineering, APIs, Git, and containers before moving into ML, LLMs, RAG, and agents. It is an advanced program, so prior programming experience helps.
What will I learn in the AI Engineering: Generative AI & Agentic AI course?
You'll learn: Transformer architecture, embeddings, and vector databases; Retrieval-augmented generation (RAG) and its common variations; Agentic AI: tools, memory, and multi-agent orchestration patterns; LLM evaluation, observability, and containerized deployment.
What tools and technologies does the AI Engineering: Generative AI & Agentic AI course cover?
This course covers: PyTorch, Hugging Face, LangChain, Ollama, CrewAI, Docker, Claude / ChatGPT.
Is the AI Engineering: Generative AI & Agentic AI course beginner-friendly?
This is a advanced-level course, so it assumes some prior familiarity with the subject.
How long does the AI Engineering: Generative AI & Agentic AI course take to complete?
The course is designed to be completed in approximately 10 weeks, depending on your pace.
What enrollment options are available for AI Engineering: Generative AI & Agentic AI?
Live Class (₹12,999) — Instructor-led live cohort with doubt-clearing sessions — includes both the course and internship certificates. Recorded Batch (₹7,999) — Full recordings of a completed live batch — includes both the course and internship certificates. Internship (₹2,499) — Work through real projects with our team and earn an internship certificate (course certificate not included). Self-Paced (₹4,999) — Learn anytime with the full recorded curriculum and community access.
Is there an internship option for AI Engineering: Generative AI & Agentic AI?
Yes — enrolling in the Internship track (or the Live/Recorded tracks, which include it) means working through real projects with our team and earning a separate Internship Certificate, in addition to the course completion certificate for Live/Recorded enrollments.
Will I get a certificate after completing AI Engineering: Generative AI & Agentic AI?
Yes. Self-paced enrollment earns a course completion certificate once your final project is reviewed and approved. Internship enrollment earns an internship certificate once your internship projects are approved. Live and Recorded enrollments earn both. Every certificate is publicly verifiable on our website and can be added directly to your LinkedIn profile.
What kind of projects will I build in AI Engineering: Generative AI & Agentic AI?
You'll work on real projects such as: A fine-tuned model adapted to a custom domain using transfer learning; A retrieval-augmented chatbot answering questions grounded in a real document set; A multi-agent system where specialized agents collaborate on a research or verification task; A containerized, deployed AI application with basic evaluation and observability in place.
Is placement support included with AI Engineering: Generative AI & Agentic AI?
Yes — every enrolled student gets full access to our placement support: resume building, a shareable project portfolio, mock interview preparation, and guidance applying what you've built to real job applications. This is support and access, not a guaranteed job outcome — how quickly it leads to an offer depends on the market and your own effort.
