IBM GenAI Course Journey: Atul's Honest Experience in 2026

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From Enrolling in a GenAI Program to Securing a Job with Coding Ninjas

Generative AI is becoming an important part of modern software development but learning how to use AI tools is different from understanding how AI-powered applications are built. For professionals looking to move into GenAI-focused roles, the challenge is often finding a structured path that combines technical learning, practical projects and career preparation.

Atul faced a similar situation. He wanted to build stronger skills in Generative AI and move beyond basic AI tools into areas such as Large Language Models, Retrieval-Augmented Generation, AI agents and Multi-Agent Systems. At the same time, he wanted guidance on projects, resume preparation and technical interviews.

After exploring his options, Atul enrolled in the Advanced Certification in GenAI & Multi Agent Systems in collaboration with IBM.

His journey involved much more than completing course modules. From his first conversation with the counsellor and learning with mentors to building projects, preparing for interviews and eventually receiving a job offer through Coding Ninjas career support, each stage contributed to his transition.

IBM GenAI and Multi-Agent Systems Program Overview

Before and After Enrolling in Coding Ninjas

Why He Wanted to Learn Generative AI

Atul had realised that Generative AI was becoming increasingly relevant to software development. However, using an AI chatbot was very different from understanding how AI applications were designed and implemented.

He wanted to understand what happened behind the interface.

As he explored the field, concepts such as LLMs, embeddings, semantic search, RAG and AI agents became increasingly important. He realised that simply learning how to write prompts would not give him the technical depth he wanted.

He was looking for a structured learning path that could help him understand how these technologies worked together to build practical applications.

That became one of the main reasons he started exploring GenAI programs.

Why Atul Chose Coding Ninjas

Before enrolling, he wanted to understand exactly what the program offered and how it could support his career goals.

A few things stood out to him:

• A curriculum covering GenAI, LLMs, RAG, vector databases, AI agents and Multi-Agent Systems
• Hands-on projects rather than only theoretical learning
• Live classes and doubt support
• IBM learning content and certifications
• Mentor and industry expert guidance
• Resume reviews and mock interviews
• Career support during the job search

He was particularly interested in having support throughout the learning process instead of trying to solve every technical or career-related problem independently.

Understanding the Program Before Enrolling

Atul's first interaction with Coding Ninjas was with a counsellor. Rather than immediately making a decision, he wanted to understand the course structure, learning format, technical curriculum and career support.

During the conversation, he discussed areas such as the GenAI curriculum, live learning, projects, IBM certifications, mentor support, resume preparation and the placement process. The conversation helped him understand that the program involved both technical learning and career preparation.

For him, this was important because he was not simply looking for another collection of recorded videos. He wanted a structured roadmap and access to people who could guide him when he faced difficulties. After understanding the program and what would be expected from him as a learner, he decided to enrol.

Starting GenAI Learning Journey

Once enrolled, Atul's focus shifted from deciding what to learn to following a structured roadmap.

The program gradually introduces learners to Generative AI and then moves towards more advanced concepts. His learning journey covered areas including LLMs, API integration, embeddings, semantic search, Retrieval-Augmented Generation, vector databases, AI agents and Multi-Agent Systems.

This progression helped him understand how individual concepts fit into a complete AI application. Instead of learning each technology separately, he could see how different components could work together to create an AI-powered system.

Learning Support When I Got Stuck

Learning GenAI can become challenging when theory turns into implementation. A project might involve APIs, embeddings, vector databases, retrieval pipelines or agent workflows and a problem in one component can affect the entire application. Atul found learning support useful during these situations.

He could use the available doubt-resolution channels when he needed clarification, while mentors and industry experts provided additional guidance during his learning journey. The program also includes Ninja AI support, Relationship Manager assistance and 1:1 industry expert sessions.

Having these support options meant that getting stuck on a concept did not necessarily mean stopping his progress for several days. He could ask questions, understand where he was going wrong and continue working on the problem.

Building Hands-On GenAI Projects

Projects became an important part of Atul's learning experience. Instead of treating projects as assignments to complete after finishing the curriculum, he used them to understand how GenAI concepts could be applied to practical problems.

The program includes 30+ AI projects and applications. These include examples such as a Scam Detection System, Smart Helpdesk Assistant and Custom Agentic AI Ecosystem.

Working on such projects helped him think about practical questions:

• How can an LLM interact with external information?
• How do embeddings support semantic search?
• When should RAG be used?
• How can a vector database support an AI application?
• How can multiple agents coordinate?
• How can tools be integrated into an AI workflow?

This practical experience later became useful during interviews because he could explain concepts using projects he had actually worked on.

Understanding Multi-Agent Systems

One of the areas he found particularly interesting was Multi-Agent Systems. Initially, concepts such as AI agents, agent orchestration and tool integration seemed like extensions of Generative AI. As he progressed, he began understanding how different agents could be designed to handle specific tasks and work together as part of a larger workflow.

This changed how he thought about AI applications. Instead of only asking how to use an LLM, he started thinking about how an AI-powered system could be designed, how different components could communicate and how tasks could be distributed between agents.

This was particularly relevant to his goal of building skills for AI-focused software roles.

Building a Portfolio and Getting Project Feedback

Atul also realised that completing projects was only one part of the process. He needed to present those projects effectively. Mentor feedback helped him understand how to improve his project explanations, documentation and overall portfolio.

He focused on being able to explain:

• The problem his project solved
• The technologies used
• How the system worked
• Why particular AI components were selected
• Challenges faced during development
• How the final application could be improved

This made it easier for him to discuss his projects during interviews. Rather than simply listing RAG or AI agents as skills on his resume, he could explain how he had applied those concepts.

Preparing My Resume for AI Roles

As Atul progressed through the technical curriculum, his attention also shifted towards his professional profile. He realised that learning new technologies would not automatically communicate his capabilities to recruiters.

His resume needed to clearly show his technical skills, projects and relevant experience. The career-support process helped him work on areas such as:

• Resume structure
• Technical skills
• Project descriptions
• Portfolio presentation
• Positioning his existing experience
• Highlighting relevant GenAI work

This was an important transition because he was moving from learning technologies to presenting himself as a candidate for relevant roles.

Preparing for Technical Interviews

Interview preparation became another important stage of the journey. He knew that knowing definitions was not enough. He needed to explain concepts clearly, discuss his projects and communicate his approach while solving technical problems. Mock interviews gave him an opportunity to practise.

He prepared around questions such as:

• How does RAG work?
• What are embeddings?
• Why are vector databases used?
• How would you evaluate a RAG system?
• What is an AI agent?
• How can multiple agents work together?
• How would you design a GenAI application?
• What challenges can arise when building AI applications?

Practising these conversations helped Atul become more comfortable discussing the technologies he had learned.

Starting the Placement Process

Once Atul had strengthened his technical profile, resume and interview preparation, he started actively exploring job opportunities. This was where Coding Ninjas' career support became an important part of his journey. The placement process was not simply about submitting applications. He had to identify suitable roles, apply, prepare for interviews and continuously improve based on feedback.

The career team supported him through this process by providing guidance and helping him navigate relevant opportunities. This made the transition from learning to job searching more structured.

From Applications to Interviews

Atul eventually started receiving opportunities to participate in recruitment processes. His projects became particularly useful during technical discussions.

When interviewers asked about his GenAI experience, he could discuss the applications he had built and explain the reasoning behind his technical decisions. He could talk about the problem, the architecture, the AI components involved and the challenges he encountered.

The preparation he had done through project discussions, resume reviews and mock interviews helped him approach these conversations more confidently.

How the Placement Team Supported Me

The placement team's role extended beyond simply sharing job opportunities. The support included several parts of the recruitment journey.

Resume Preparation: His resume was reviewed to make his skills and projects clearer to recruiters.

Career Guidance: He received guidance on the types of roles he could target based on his skills and experience.

Interview Preparation: Mock interviews helped him practise technical and communication skills.

Job Opportunities: The career team helped him discover and navigate relevant opportunities.

Ongoing Support: Atul could seek guidance during different stages of the job-search process instead of handling every step independently.

This support helped him connect his learning experience with the actual recruitment process.

Finally Receiving the Job Offer

After going through the recruitment process and interviews, Atul eventually secured a job offer from a top company through Coding Ninjas career support. For him, the offer represented the outcome of several stages rather than a single event.

Learning → Projects → Portfolio → Resume → Mock Interviews → Applications → Interviews → Job Offer

The technical curriculum gave him the foundation to discuss GenAI concepts. Projects provided practical examples. Career preparation helped him present those skills, while placement support helped him navigate the job-search and interview process.

His experience also reinforced the importance of staying involved throughout the journey. Career support can provide guidance and opportunities but the learner still needs to practise, apply, attend interviews and continue improving.

What I Learned From My Coding Ninjas Experience

Atul's journey highlighted several lessons for anyone considering a similar transition.

GenAI Requires Consistent Practice

Generative AI is a broad field and trying to learn everything simultaneously can become overwhelming. Following a structured roadmap helped him focus on one concept at a time.

Projects Make Learning Practical

Working on projects helped him move from theoretical understanding to implementation. They also gave him practical examples to discuss during interviews.

Career Preparation Should Start Early

Resume preparation and interview practice should not be left until the end of the program. Working on them alongside technical learning gave him more time to improve.

Asking Questions Can Save Time

Mentor guidance and doubt support helped him overcome technical challenges without spending excessive time trying to solve every problem alone.

The Job Search Requires Active Participation

Career support can provide guidance and opportunities but learners still need to apply consistently, prepare for interviews and take ownership of their job search.

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