Question 1

How does this AI engineering course help engineers move from AI concepts to production systems?

Accepted Answer

This AI engineering course is built for engineers who want practical experience building AI applications. Over 3 months, we cover ML and deep learning foundations, LLM engineering, RAG pipelines, agentic AI, evaluations, observability, and deployment workflows through live sessions and hands-on implementation.

As you progress through the program, you’ll work on projects involving function calling, fine-tuning, multi-agent workflows, tracing, monitoring, and deployment. Our main focus is on helping you understand how modern AI systems are built, tested, and deployed in production settings.

By the end of the program, you’ll have worked on multiple hands-on AI projects that you can add to your portfolio along with experience using tools and workflows commonly used in production AI teams.

Question 2

Why is the IIT Roorkee x Scaler AI program positioned as an advanced AI engineering course?

Accepted Answer

The IIT Roorkee x Scaler AI program has been positioned as an advanced AI engineering course because it covers multiple stages of modern AI development as we understand that just including theory based subjects won't be enough.

Some of the key reasons include:

The curriculum covers ML foundations, deep learning, LLM engineering, RAG, agentic AI, evaluations, observability, and deployment.
The program runs across 3 months with 90+ hours of learning and 32 live sessions.
Learners get to work on hands-on AI projects throughout the course instead of only theory-based learning.
Sessions are conducted by IIT Roorkee faculty along with Scaler instructors from the industry.
The program includes practical exposure to workflows and tools used while building AI applications.
Learners will receive an AI certification course by IIT Roorkee through CEC, IIT Roorkee, offered in partnership with Scaler.

Question 3

What will I be able to build by the end of this AI engineering program?

Accepted Answer

Our course includes practical projects across different areas of AI. During the program, you’ll work on a medical image classifier, a RAG-powered support bot, a fine-tuned domain model, and a deployed multi-tool AI agent.

Since this will be an AI engineering course with projects, the focus will stay on helping you build projects that demonstrate your skills clearly. By the end of the program, you’ll have AI projects that you can showcase in your portfolio, interviews, or professional profile.

Question 4

How does this program go beyond a typical Generative AI or prompt engineering course?

Accepted Answer

It is true that most generative AI course options mainly focus on prompts and chatbot usage. And we understand that most students require more than that. Hence, we have made sure to cover prompt engineering in this AI prompt engineering course, but the learning extends into the systems used to build and manage AI applications.

You’ll work with LLM APIs, structured outputs, embeddings, RAG pipelines, fine-tuning, agentic AI systems, evaluations, observability tools, and deployment workflows.

Question 5

How deep does the course go into LLM engineering and real-world LLM applications?

Accepted Answer

A significant part of the curriculum focuses on LLM engineering and how modern AI applications are built around large language models. Learners will work with OpenAI APIs, ChatCompletion APIs, multimodal APIs, function calling, structured outputs, embeddings, and RAG pipelines during the program.

The syllabus also includes topics like prompt caching, batching APIs, cost and latency control, fine-tuning, and distillation because we understand that learners should know how LLM applications are managed beyond the experimentation stage. Along with the concepts, learners will also evaluate AI applications using offline and online evaluation methods while working on practical implementations.

Question 6

How will learners build and evaluate a production-style RAG application?

Accepted Answer

During the program, learners will build a RAG-powered support bot while understanding how different layers of a retrieval augmented generation application work together. The project includes embeddings, vector search, chunking strategies, retrieval architecture, function calling, memory, and guardrails.

The course also spends time on how these RAG systems are evaluated and improved over time. Learners will work with offline and online evaluation methods while testing how their applications retrieve, process, and generate responses. Since the project is built step-by-step during the program, learners get to understand how practical RAG workflows are managed instead of only seeing a finished demo.

Question 7

How much Agentic AI will this course cover, and will learners deploy an AI agent?

Accepted Answer

Agentic AI is covered through two dedicated sessions focused on planning, reflection, tool use, frameworks, failure modes, and multi-agent orchestration. We understand that AI agents have become an important part of modern AI systems, which is why the program spends dedicated time on how these workflows actually operate.

Learners will also work on a deployed multi-tool agent capstone project during the program. Along with building the agent, the curriculum includes traces, observability, online evaluations, regression testing, monitoring, and deployment workflows so learners can understand how AI agents are managed after deployment as well.

Question 8

What production AI skills are covered beyond model building?

Accepted Answer

This production AI course also focuses on the systems and workflows used after an AI application moves beyond experimentation.

Learners will work with:

traces and online evals
cost tracking and latency control
regression testing
deployment workflows
monitoring systems
LLM observability

The curriculum also includes a two-part deployment workshop where learners go live with an AI agent while understanding production monitoring workflows.

Question 9

Who is this AI Course built for: software engineers, data professionals, or tech leads?

Accepted Answer

Our AI course is for working professionals; it is mainly suited for software engineers, data scientists, DevOps professionals, tech leads, and managers who want an extensive exposure to modern AI systems and workflows.

We have also kept the program suitable for experienced technical professionals who are familiar with programming and problem-solving, including serious early-career professionals looking to move deeper into AI engineering.

Question 10

Is this course suitable for experienced engineers who have not worked deeply in ML before?

Accepted Answer

Yes. We understand that many experienced engineers may already be strong in software development or system design without having worked deeply in ML before. That is why the program starts with ML and deep learning foundations, then gradually moves into LLM engineering, RAG systems, evaluations, agentic AI, and deployment workflows. While prior ML exposure can help, learners should still be comfortable with programming and technical problem-solving since the curriculum becomes more advanced as the program progresses.

Question 11

How does the IIT Roorkee CEC certificate add value to a learner’s professional profile?

Accepted Answer

On successful completion of the program, learners will receive an IIT Roorkee CEC certificate issued by the Continuing Education Centre, IIT Roorkee, in partnership with Scaler.

The certificate can be added to LinkedIn, resumes, and professional profiles as part of a learner’s AI engineering journey.

At the same time, we understand that a credential alone is not enough in AI hiring conversations today. That is why the program also focuses heavily on hands-on projects, evaluations, deployment workflows, and practical AI implementation throughout the curriculum. This will help the IIT Roorkee AI certificate stay connected to demonstrable work and technical learning.

Question 12

What does the 3-month learning journey look like from foundations to deployment?

Accepted Answer

The advanced AI engineering course follows a phased learning structure across 3 months and 32 live sessions.

Month 1 focuses on ML foundations, deep learning, neural networks, computer vision, and transformers.
Month 2 moves into LLM engineering, embeddings, RAG systems, function calling, fine-tuning, and evaluations.
Month 3 covers Agentic AI, multi-agent orchestration, observability, deployment workflows, and a deployed AI agent project.

Question 13

How should learners think about the IIT Roorkee AI course fees and what is included?

Accepted Answer

The IIT Roorkee AI course fees for the complete 3-month program are ₹76,699 + GST, subject to final verification before publishing.

The fees include:

90+ hours of learning
32 live sessions
hands-on AI projects and workshops
learning across LLM engineering, RAG, Agentic AI, evaluations, and deployment workflows
IIT Roorkee CEC certificate on successful completion

The optional 2-day IIT Roorkee campus immersion is priced separately at ₹1,500 per day + GST.

Question 14

What makes the optional IIT Roorkee campus immersion valuable?

Accepted Answer

The optional IIT Roorkee campus immersion gives learners a chance to spend 2 days inside the IIT Roorkee campus environment while participating in hands-on learning sessions, networking activities, research lab visits, and fireside chats with industry experts. As part of the IIT Roorkee AI course experience, learners also get access to IIT-R facilities and the opportunity to interact with peers from similar technical backgrounds. Food and accommodation are included during the immersion, and the fee is kept separate at a nominal ₹1,500 per day + GST.

Question 15

How will this course help learners talk about AI engineering in interviews or internal role transitions?

Accepted Answer

The program focuses heavily on practical AI engineering skills like RAG architecture, LLM evaluations, AI agents, fine-tuning, observability, deployment workflows, cost and latency trade-offs, and production monitoring. Along with the concepts, learners also build hands-on projects throughout the program, which gives them concrete examples and implementation experience they can discuss professionally later on.

Question 16

How is AI engineering different from data science, and where does this course fit?

Accepted Answer

You can understand the difference between data science vs AI engineering by looking at the kind of work involved in both areas. Data science is often more focused on analysis, experimentation, modelling, dashboards, predictions, and extracting insights from data.

AI engineering focuses more on building AI-powered applications and workflows that users or teams can actively interact with. That includes areas like LLM applications, RAG systems, AI agents, evaluations, deployment workflows, observability, and production monitoring.

This AI engineering course is better suited for learners who want to work closer to AI products, application workflows, and production systems rather than only analytics or experimentation.

Question 17

When should teams use RAG, fine-tuning, or AI agents in real AI products?

Accepted Answer

Different AI systems solve different kinds of problems, which is why the program spends time helping learners understand where RAG, fine-tuning, and agentic AI are actually useful.

RAG is commonly used when applications need access to external or constantly changing knowledge sources.
Fine-tuning becomes useful when teams want stronger domain-specific behaviour or more consistent model outputs.
Agentic AI is more suitable for workflows involving planning, tool usage, multi-step actions, or decision-making systems.

Since many modern AI products combine these approaches together, learners will also work with all three during projects and workshops throughout the program.

Question 18

How does the course teach reliability in GenAI applications?

Accepted Answer

GenAI applications can often produce inconsistent outputs, which is why the program also covers GenAI evaluation, reliability workflows, and monitoring systems as part of the curriculum.

Learners will work with:

LLM evaluation methods
LLM observability
traces and monitoring
online evals
cost tracking
regression testing
latency and deployment workflows

The idea is to help learners understand how AI systems are evaluated, monitored, and improved once they start interacting with real users and production environments.

Question 19

What kind of peer group can learners expect in this AI engineering cohort?

Accepted Answer

Since it is an AI course for working professionals, it is aimed to attract experienced software engineers, data scientists, senior ICs, tech leads, architects, and engineering managers across past cohorts. Many learners have already worked on large-scale systems and products before joining the program.

For learners exploring an AI course for senior engineers, the peer group becomes an important part of the experience because discussions often go beyond theory into architecture decisions, deployment trade-offs, implementation challenges, and real engineering workflows.

Question 20

Is this program right for someone looking only for a free or beginner-level AI introduction?

Accepted Answer

We don’t suggest this program to beginners. While the curriculum starts with foundations, this is not meant to be a casual AI course for beginners or a free introductory overview of AI tools. The program is better suited for learners who are serious about building AI systems and are willing to spend time working through hands-on projects, evaluations, deployment workflows, RAG systems, and AI agents. For AI course eligibility, learners should also be comfortable with programming and technical problem-solving before joining the program.

Question 21

Will this program help me use AI for software engineering productivity?

Accepted Answer

Yes. The program shows how LLMs can be used to support everyday software engineering work, including system design tasks and AI-assisted coding workflows similar to tools like Cursor and Copilot.

AI Engineering Course with Gen AI & LLM's Certification by IIT Roorkee, CEC

The AI stack is moving fast. Get hands-on with Machine Learning, Generative AI, LLMs, RAG, and Agentic AI built for students ready to lead and professionals ready to evolve.

Industry Ready Business And AI Skills

3 months with hands-on projects

Optional IIT-R Campus Immersion

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Learn from the top 1% of practitioners.

Deep Learning and Large Language Models

Fine Tuning and Distillation

Deep Learning and Large Language Models

Fine Tuning and Distillation

Designed to make you ready for the next decade in AI

Every project ships with an AI layer. That's the new bar.

Medical Image Classifier

RAG-Powered Support Bot

Fine-Tuned Domain Model

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Medical Image Classifier

RAG-Powered Support Bot

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