Offered by Department of Mechanical Engineering, IIT Bombay

This is an instructor-led certificate delivered through weekly live sessions, guided labs, and a Capstone Project.

The certificate is delivered by faculty from the Department of Mechanical Engineering at IIT Bombay. It is led by Prof. Shivasubramanian Gopalakrishnan, whose work lies at the intersection of High-Performance Computing (HPC) and AI-driven scientific computing.

The curriculum is divided into four modules:
Module 1: Fundamentals of AI, ML, and HPC: Covers hardware-aware programming, parallel computing fundamentals, OpenMP, and Message Passing Interface (MPI).
Module 2: Containerisation, Orchestration and Management: Focuses on orchestrating multi-node clusters, managing containerisation, job scheduling, and distributed deep learning frameworks.
Module 3: Training Large Models: Explores infrastructure requirements for large-scale model training, model parallelism strategies, parallel I/O, and ML model serving.
Module 4: Enterprise MLOps and Cloud Infrastructure Strategy: Covers CI/CD pipelines, production monitoring, and cloud-HPC integration.

Yes. You will work on real-world projects such as parallelizing Machine Learning algorithms, configuring distributed model training systems across clusters, and developing scalable inference systems.

You will build practical systems, and some sample projects include:

• Parallel Optimisation for ML Algorithm: Parallelising a machine learning algorithm using shared memory techniques.
• Deploying a Distributed Model Training System: Configuring a multi-node training job using a containerised environment and a job scheduler.
• Capstone Project: The certificate concludes with a Capstone Project, which may involve conducting a large-scale model scalability study or building an end-to-end MLOps pipeline.

LLMs are covered as part of:

• Scaling, serving and operating large-scale models
• Model parallelism strategies
• Infrastructure requirements

Focus is on how to train and scale them efficiently, not just use APIs.

In this course, the emphasis is on learning techniques. You will build and run hands-on labs on your own machine and scale to cloud resources as required.

Yes, projects involve:

• Large datasets
• Distributed training systems
• Scalable pipelines

Including end-to-end MLOps pipelines and high-throughput systems.

The curriculum provides hands-on experience with industry-standard tools, including:

• Parallel Programming: MPI (MPICH, Intel MPI) and OpenMP.
• ML Frameworks: PyTorch and TensorFlow.
• Containerisation & Orchestration: Docker, Kubernetes, and NVIDIA Container Toolkit.
• HPC & MLOps Tools: Slurm, PBS, Intel VTune, Git, and GitHub Actions.

Most MLOps training courses focus on: Model building and Algorithms
This Certificate in AI Engineering and MLOps by IIT Bombay focuses on: Scaling models, distributed systems, and MLOps and deployment. It addresses the industry shift from model selection to model scaling.

Learners should have a Bachelor's or Master's degree in Engineering from a recognized university with a minimum aggregate of 50% (or equivalent CGPA) and a minimum of 2 years of relevant professional work experience.

Yes. The online curriculum is specifically designed for working professionals and features weekly live sessions to accommodate their schedules.

Yes. Prior exposure to Python or C/C++ programming is required, along with familiarity with the Linux command line, bash scripting, and experience with version control (Git).

Yes. The Certificate in AI Engineering and MLOps is highly suitable for Software Development and Engineering professionals aiming to transition to AI/ML Engineering roles by learning about distributed training and parallel programming.

The total fee is ₹1,65,000 + 18% GST.

Financial assistance is provided through partners like Propelld, Zest, Eduvanz, and LiquiLoans, subject to conditions. You may connect with the Registration Team for details.

1. Application: Interested candidates can apply by filling out a simple online application form.
2. Interview Process: Go through a mandatory screening call with the registration office.
3. Offer of Registration: Selected candidates will receive an offer letter. They must pay the registration fee to confirm their seat and complete the registration.

This certificate prepares you to:

• Transition from standard data or software roles into specialised AI Engineering positions.
• Leverage high-performance computing to accelerate training and manage large datasets.
• Drive the reliable deployment of ML workloads across cloud and hybrid environments.
• Master distributed training, GPU optimisation, and MLOps orchestration.

No. The certificate curriculum is online and specifically designed for working professionals.

An MLOps Engineer focuses on deployment, automation, and lifecycle management of ML systems, while an AI Engineer combines both model building and system scaling.
This certificate bridges both roles by covering:

• Model training (AI)
• Deployment & monitoring (MLOps)

Software Engineers already possess a strong foundation in programming and system design. To transition into AI Engineering, you need to bridge the gap by learning distributed model training, parallel programming (MPI, OpenMP), and orchestration tools like Kubernetes and Docker. The IIT Bombay Certificate in AI Engineering and MLOps is specifically designed to facilitate this exact transition.

Yes. As artificial intelligence models increase in complexity and size, the primary challenge for global organizations has shifted from simply selecting models to successfully scaling them. Professionals who can manage distributed training, GPU optimisation, and enterprise MLOps are among the most in-demand talent in the tech industry today.
To build these capabilities, many aspiring professionals seek an Artificial Intelligence and Machine Learning engineering course, which provides hands-on experience in model development, deployment, and real-world AI systems.

An AI Engineer builds, scales, and deploys machine learning systems in production. Typical responsibilities of an AI Engineer include:

• Designing scalable ML systems
• Deploying models using MLOps pipelines
• Managing distributed training on clusters
• Monitoring model performance in production

To become an AI Engineer in India, you need a strong foundation in programming, Machine Learning, and system design, followed by hands-on experience in building and deploying scalable AI systems.
This certificate helps you transition by focusing on distributed training, parallel computing, and MLOps, which are essential for real-world AI deployment.

The primary AI engineer requirements involve a mix of Software Engineering and Data Science. Key skills include:

• Machine learning model development
• Distributed computing and parallel programming
• Data pipeline engineering
• Model deployment and monitoring (MLOps)

The curriculum specifically teaches MPI, OpenMP, Kubernetes, CI/CD pipelines, and scalable AI systems.

The program prepares you for production AI roles, not just modeling roles. You can transition into roles such as:

• AI Engineer
• MLOps Engineer
• ML Infrastructure Engineer
• Applied AI Engineer

While MLOps borrows core principles from DevOps—such as continuous integration and continuous delivery (CI/CD)—it is uniquely complex. DevOps focuses primarily on code deployment. MLOps must go further to encompass data ingestion, model training, tracking dataset versions, managing compute resource allocation, and monitoring model degradation over time.

Yes. Successfully implementing MLOps requires strong technical skills. Professionals need proficiency in programming languages like Python or C/C++, familiarity with Linux/Bash scripting, and hands-on experience with version control systems (Git) and containerisation platforms (Docker).

Yes, there is an acute shortage of specialized AI Engineers. While many professionals know how to build basic Machine Learning models, very few possess the technical expertise to scale those models into production environments. Companies are actively seeking engineers who can handle parallel algorithms, distributed training, and automated CI/CD pipelines to make their AI investments profitable and reliable.

Yes, particularly when it is offered by a premier institution and focuses on hands-on implementation. The Certificate in AI Engineering and MLOps from IIT Bombay is highly valued because it goes beyond theory. Learners build a portfolio of real-world projects—such as configuring multi-node training jobs and deploying end-to-end MLOps pipelines—proving to employers that they can handle enterprise-grade infrastructure.

Absolutely. Cloud and DevOps professionals are in an excellent position to transition into AI Engineering. They already understand infrastructure, continuous integration, and server management. By taking a program like the IIT Bombay Certificate in AI Engineering and MLOps, they simply need to learn how to adapt their existing orchestration skills to specialized AI workloads, distributed deep learning frameworks, and GPU optimisation.

For professionals who already possess a background in software development or data, transitioning into AI Engineering typically takes about five to six months of targeted learning. Comprehensive programs, such as the 5-month Certificate in AI Engineering and MLOps from IIT Bombay, provide the accelerated, hands-on experience needed to master high-performance computing without requiring a multi-year degree.

No, a Master's degree is not strictly required if you have demonstrable skills in building scalable AI infrastructure. Employers prioritize hands-on experience with industry-standard tools like PyTorch, Kubernetes, and automated CI/CD pipelines. An advanced certificate from a premier institution like IIT Bombay, combined with a strong portfolio of deployed MLOps projects, is highly effective for transitioning into this role.

Building models is only a small part of the AI lifecycle.
MLOps is required to:

• Deploy models reliably
• Automate retraining and updates
• Monitor performance in production
• Without MLOps, models remain experimental and cannot deliver business value