Applied Gen AI & Agentic AI Bootcamp
Specialize in the highest demand skill in tech right now: LLMs, agents, and production AI systems.
10% launch discount applied · EMI option available
- Live mentor-led sessions
- Assessments & project reviews
- 3-month internship
- Certificate of completion
- AI Interview + AI Resume tools
- Placement assistance up to 12 months
Who this course is for
- Graduates and engineers who want to specialize in applied GenAI
- Developers looking to move into AI/ML-focused roles
- Anyone who wants to build, not just prompt, AI products
Curriculum
- How LLMs actually work
- Prompt engineering that holds up in production
- Model selection & cost trade-offs
Capstone project
A deployed AI product, such as an agent or RAG assistant, with a demo you can walk an interviewer through.
Internship & certification
3-month internship
After your capstone, you'll complete a 3-month internship applying your Gen AI & Agentic AI Bootcamp skills to real project work, guided by your mentor, so you graduate with experience, not just a completed course.
Certificate of completion
Once you finish the course and internship, you get a verifiable certificate for your resume and LinkedIn.
Tools & technologies
Career outcomes
Where this track gets you hired
Hiring partners who actively recruit for the roles this track trains you for.
Real Projects You'll Work On
The Multi-Doc RAG System
Build a RAG system over multiple real documents with hybrid (keyword + vector) retrieval and citation tracking, so every answer points back to its source.
The Retrieval Bench
Stand up multiple RAG pipelines over the same document set and benchmark them with a real evaluation rubric, not eyeballed answers.
The Support Deflector
Build an AI support agent that triages and resolves real tickets end-to-end, escalating only what it should.
The Multi-Agent Orchestrator
Chain planner, retriever and executor agents together, documenting the tool choices and guardrails that decide when the agent should not act.
The Fine-Tune Lab
Fine-tune an open-source LLM for a narrow task and evaluate it head-to-head against the base model.
The Deployed Capstone
Ship your GenAI product behind a FastAPI or Streamlit interface, containerized with Docker, so it's demoable with one link, not a notebook.
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