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IIT Roorkee's Advanced PG Certificate in AI Engineering on Cloud and AIOps

Engineer AI Systems for Top Enterprises—with the Most Critical Skills of the Decade

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Where the Real Hiring Is Happening

India’s Only Certificate Covering the Full AI Engineering Lifecycle

Master 15+ Real-World Tools, Frameworks & AI Workflows

Learn from IIT Faculty & CXOs Behind Scalable AI Systems

High-Growth Job Paths in AI Engineering

Scale Enterprise AI on Cloud

GenAI Lit the Spark: But It Couldn’t Sustain the Fire

  • By 2024, every team had their GenAI moment: chatbots, copy tools, HR prompts.
  • But 90% of pilots never reached production.(Gartner, 2025). Because isolated team AI can’t scale... only orchestrated systems can.

The Root Problem? Teams Were Building in Isolation

A marketing model can’t talk to HR. A chatbot can’t update fraud systems.

‍

Disconnected tools. No system integration. Without orchestration, even the best model breaks.

30% of GenAI projects were abandoned post-demo. (TechRepublic)

Enterprise AI Took Over: and It’s Winning

Companies are now building Enterprise Agentic AI Systems... cross-functional, autonomous, updated.

‍

Amazon’s supply chain restocks warehouses autonomously.Mastercard’s AI blocks 90% of fraud in milliseconds.Flipkart self-heals its infra during festive crashes. Infact,Agentic workflows deliver 30% higher ROI (McKinsey)

India’s Big Bet: Cloud + GPU + Agentic Infrastructure

The IndiaAI Mission now runs 34,333 GPUs nationwide. Indigenous GPU manufacturing begins by 2029. Datacenters like CtrlS and Yotta offer on-demand AI compute at ₹67/hour

Cloud-Native AI Is Now the Default

90% of new AI workloads run on AWS, GCP, or Azure.

‍

Why? Why?

₹200 Cr to buy.(1000 GPUs), ₹5L/hour to rent.

Elastic, scalable, cost-effective

(IMARC, 2025)

Legacy Enterprises Need Full-Cycle AI Engineering

India’s real AI race ≠ startups

‍

It’s in legacy enterprises + GCCs

60% of Indian banks still run on 1990s systems (RBI, 2025)

Why?

Fragmented data + hardcoded logic + outdated infra

It’s time to convert prototypes into pipelines

Today, AI ≠ Just Model Building
→ It’s Build + Deploy + Scale + Maintain

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This Is Where the Real AI Hiring Begins

Legacy enterprises and GCCs are actively hiring engineers who can build elastic, cloud-native AI pipelines...
Not just models, but systems that scale, adapt, and perform in production.

From Coder to System Thinker... This Is the Program

About IIT Roorkee

IIT Roorkee is one of India’s most prestigious and oldest technical institutions, with a legacy spanning over 175 years. Ranked #8 overall and #6 in engineering in NIRF 2024, it also holds the #1 spot in architecture and is globally recognized, ranking around 900 in QS World University Rankings and 800 in US News Global Rankings. The institute has earned top honors such as the ENCORE Awards for student excellence, Prime Minister Research Fellowships, and the David Miller Young Scientist Scholarship. Accredited with an 'A++' grade by NAAC and recognized as an Institute of National Importance, IIT Roorkee is renowned for its pioneering research in AI, robotics, and sustainable engineering. With state-of-the-art labs, global collaborations, robust startup incubation, and a strong alumni network, it stands out as a premier hub for deep tech innovation and technological leadership in India.

Director
Program Director

About Director

Prof. Kamal Kishore Pant is the Director of IIT Roorkee, appointed in October 2022. A distinguished chemical engineer, he holds BTech, MTech, and PhD degrees from IIT Kanpur and HBTU Kanpur. Previously, he served as Dean and Head of Chemical Engineering at IIT Delhi. Prof. Pant is renowned for his research in hydrogen generation, CO₂ capture, e-waste management, and catalysis, with over 220 publications, 11,700+ citations, and several patents. He has led major projects worth over ₹100 crore and received honors such as the Gandhian Young Technological Innovation Award and CHEMCON Distinguished Speaker Award. Under his leadership, IIT Roorkee continues to excel as a center for deep tech research and innovation

Program Director

Prof. Neetesh Kumar is an Associate Professor at IIT Roorkee and one of India’s leading voices in applied AI, cloud systems, and intelligent infrastructure. With multiple patents, global research citations, and editorial leadership at IEEE’s top AI journal, his work bridges the gap between academic depth and industry impact.With 180+ citations on intelligent traffic systems, multiple patents, and a position on the editorial board of IEEE’s top-ranked journals, he knows exactly where AI is headed: cloud-native, production-grade, real-world systems.That’s why this program isn’t academic theory, it’s a hands-on blueprint for the AI engineer India needs next. From deep research in parallel computing to running projects funded by DST, SERB, and CSIR, Prof. Neetesh brings the best of research, industry, and teaching into one classroom.He’s here not to teach code, but to train system thinkers who can lead India’s AI future.

What This Program Packs In

Everything You Need to Run Enterprise AI Systems…Start to Scale.

Apply Now

Full AI Engineering Lifecycle

India's Only Certification Covering the Full AI Engineering Lifecycle

Industry-Led Design

Designed by Industry Leaders Driving AI Hiring & Future Roadmaps

Advanced AI Technologies

Master GenAI, LLMs, MLOps, and Agentic AI Systems

Cloud-Native Focus

Built for Cloud-Native AI Deployment at Scale

Project-Based Learning

End-to-End Project-Based Learning with Real Enterprise Use Cases

Expert Faculty

132 Hours of Live Learning - 50% by Top IIT Faculty, 50% by Leading Industry Experts

Flexible Learning

Live Online Classes | Flexible Schedule | Ideal for Working Professionals

Campus Experience

Optional 3-Day Campus Immersion at IIT Roorkee

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Designed by CXOs & Leaders at Top AI-First Companies

  • Module 1: Foundations of AI ML and Generative AI

    1. Understanding Supervised Machine Learning, Unsupervised Machine Learning and Reinforcement Learning
    2. Data Manipulation for Machine Learning
    3. Exploratory Data Analysis for Machine Learning
    4. Essential Mathematics for Machine Learning and AIDefinition of Generative AI
      Generative AI Applications  
    5. Definition of LLMs

  • Module 2: Deep dive into Machine Learning Algorithms

    1. Regression Supervised Machine Learning  
      Linear Regression
      Decision Tree Regressor
      Random Forest Regressor
      Overfitting, Underfitting and Regularization
      ‍
    2. Classification Supervised Machine Learning
      Logistic Regression
      Decision Tree Classifier
      Random Forest Classifier
      ‍
    3. Unsupervised Machine Learning
      KMeans Clustering
      Hierarchical Clustering  
      Dimensionality Reduction
      Anomaly detection
      ‍
    4. Understanding Reinforcement learning: Basic concepts
      (Action, reward, process), MPD, RL, DRL, DQN, etc.
      ‍
    5. MLOps for Machine Learning model deployment

  • Module 3: Understanding Deep Learning

    1. Exploring Deep Learning
    2. Understanding Neural Networks with TensorFlow
    3. Deep dive into Neural Networks with TensorFlow
    4. Artificial Neural Networks (ANN)
    5. Convolutional Neural Networks (CNN)
    6. Object detection using YOLO framework
    7. Transfer Learning in CNN
    8. Recurrent Neural Networks (RNN) and LSTMs

  • Module 4: Exploring NLP

    1. Architechtures of RNN - One to One, Many to Many, Many to One etc
    2. Understanding Text for NLP
    3. Tokenization, Stemming and Lemmatization, Stopwords and Keywords in NLP
    4. Text Vectorization using TF-IDF, Bag of Words and Word 2 Vec
    5. Use cases across domains in NLP

  • Module 5: Engaging with Generative AI

    1. How Generative AI works
    2. Text generation
    3. Image generation  
    4. Audio generation and video generation
    5. Using Hugging face to access models for text generation, image generation
    6. GANs
    7. Types of GANs - ProGAN, SRGan, CycleGAN
    8. Auto Encoders, Variational Auto Encoders (VAEs), Diffusion Models

  • Module 6: Understanding LLMs

    1. Definition of LLMs
    2. LLM Use Cases
    3. Prompt TuningAttention Mechanism
    4. Transformer Model and architecture
    5. Encoder-Decoder arrangements
    6. Train and Generate text using Encoder-decoder Architecture
    7. BERT for Transfer learning
    8. Leveraging multiple pretrained LLMs from Hugging Face
    9. Fine tuning LLMs

  • Module 7: GenAI Application Development Prompting Techniques

    1. Introduction to Prompt Engineering
    2. Successful and Unsuccessful prompts
    3. Types of Prompting
    4. Introduction to Open AI, GPT , Open AI Playground
    5. Cost & latency considerations when calling APIs (OpenAI, Azure, AWS).
    6. Multimodal prompting for GPT 4  
    7. Image generation using Open AI DALLE 3
    8. Prompt evaluation
    9. Implementing Agents and Chains
    10. Implementing zero-shot-react, conversational-react agents with LangChain
    11. Open AI Function (Tool Integration)  
    12. Testing various LLMs with Prompt Engineering

  • Module 8: Synthetic Data and Datasets for LLMs

    1. Introduction to Synthetic Data
    2. Generating Synthetic Data
    3. Synthetic Data for LLMs
    4. Real-world Applications and Use Cases
    5. Hands-on generating and using Synthetic Data

  • Module 9: AI Embeddings & Retrieval

    1. Understanding AI Embeddings
    2. Advanced Retrieval TechniquesHugging Face Embeddings
    3. Vector Databases in AI
    4. CRUD operations with Vector Databases
    5. RAG -  Retreival Augmented Generation
    6. RAG solutions using Open AI models and Hugging face models
    7. Ethical Considerations in AI Embeddings
    8. Navigating AI Hallucinations, Drift, and BiasEmbeddings in Real-world Applications
    9. Embeddings Optimization and Fine-tuning
    10. Embeddings Security and Privacy
    11. LLM Ops and model deployment best practices

  • Module 10: Understanding Agentic AI

    1. Agents, Agentic AI and Multi-Agent Systems
    2. Agent Definition & Autonomy
    3. Simple vs. Knowledge-Based Agents
    4. Reflex vs. Goal-Driven AgentsMicrosoft AutoGen
    5. Agent Architecture (Perception, Decision, Action)
    6. Integrating Knowledge Bases (RAG, Domain Data)
    7. Measuring Performance (Success Rate, Resource Usage)
    8. Hierarchical Agent Planning
    9. Multi-Step Reasoning with LLM
    10. Memory & Long-Term Context
    11. Integrating Retrieval Augmentation in Agent Workflows
    12. Domain-Specific Knowledge & Dynamic Prompting

  • Module 11: Exploring Cloud Ecosystems

    1. Cloud Ecosystems
    2. Introduction to Cloud Ecosystem
    3. Definitions
    4. Cloud characteristics
    5. Deployment models
    6. Leading Service providers (AWS, Google, Azure, etc.)
    7. Comparing AWS, Azure, and GCP core services for compute, storage, and
    8. AI/ML.
    9. Data Centres and their components
    10. Service (SaaS, IaaS, PaaS)
    11. Issues & Challenges

  • Module 12: Advancement in Hypervisors

    1. Understanding hypervisors
    2. Reference model
    3. Virtualisation characteristics
    4. Principles of hypervisor design
    5. Interfaces
    6. Types of hypervisors (type-1 and type-2)
    7. Differences between Type-1 and Type-2 hypervisors.
    8. Design methods of hypervisors (full virtualization, para virtualization, and hardware-assisted virtualization)
    9. Memory Virtualisation
    10. I/O virtualisation
    11. OS virtualization
    12. Comparative Analysis of hypervisors
    13. Understanding performance, requirements, and bottleneck

  • Module 13: Container Orchestration

    1. Understanding LLM Deployment Architectures
    2. Containerizing LLM Inference Services (e.g., using FastAPI + Docker)
    3. Managing GPU Workloads in Kubernetes
    4. Scaling LLM APIs with Kubernetes and Istio
    5. Optimizing Latency and Throughput for LLM Containers
    6. Secure Access and Rate Limiting for AI APIs
    7. CI/CD for LLM-Powered Microservices
    8. Monitoring and Logging for LLM Containers
    9. Model Versioning and Rollbacks
    10. Cost Optimization Strategies for LLM Inference in Production

  • Module 14: Cloud Security & Resilience

    1. Infrastructure security: Network-level security
    2. Host-level security Application-level security
    3. Data security and storage: Data privacy and security issues
    4. Jurisdictional issues raised by data location
    5. Identity and access management
    6. Access control
    7. IAM, Key Management Services, and zero-trust architecture
    8. Trust, reputation, risk
    9. Authentication in cloud computing
    10. Client access in the cloud
    11. Cloud contracting model
    12. Commercial and business considerations

  • Module 15: Understanding Cloud in context of Gen AI and LLMs

    1. EC2 Deep Dive and AMIs
    2. EBS vs S3 vs EFS – Storage Solutions
    3. Load Balancing and Auto Scaling Basics
    4. Intro to Serverless: AWS Lambda
    5. Using AWS Bedrock for GenAI (including foundation models)
    6. Deploying Open-Source LLMs on EC2/EKS
    7. Fine-Tuning and Inference Pipelines on Cloud
    8. Cost & Performance Considerations for LLM Workloads
    9. SageMaker Pipelines for Model Training and Inference
    10. Model Versioning, A/B Testing, and Rollbacks
    11. Security and Compliance for GenAI in Production

  • Total

    Total Duration - 132 Hours
    Hours trained by IIT - 66 Hours
    Hours trained by Futurense - 66 Hours

Before the Job Offer, the Job Experience

Al-Powered Meeting Notes Generator

Saves time, captures action items, boosts productivity

Speech-to-text
Text summarization
Web app
Cloud deployment

GenAI-Powered Marketing Content Generator

Quickly creates ad copy, product descriptions, blogs, etc.

LLMs (OpenAl, Cohere)
Prompt engineering
Web app

Smart FAQ Chatbot

Saves HR/IT Time, Improves obloarding & support

NLP
LLMs
Prompt Engineering
Vector DB
Web App
Cloud Deployment

Automated News Summarizer

Saves research time, keeps teams updated

Web scraping
Text summarization
NLP
Web UI
Cloud deployment

Personal Finance Tracker

Empowers use, supports financial wellness

Data processing
ML
Web/mobile app,
Cloud hosting
Previous
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Is this for you

Ideal For
Eligibility Criteria

AI Engineers, Developers and Data Engineers: Looking to move from prototypes to production with GenAI, MLOps, and Agentic AI.

Cloud Developers & DevOps Engineers: Aiming to add full-stack AI deployment, observability, and orchestration skills.

Software & System Architects: Building intelligent, cloud-native systems that scale across enterprise use cases.

Tech Entrepreneurs & Founders: Creating AI-powered products and looking to fast-track engineering capabilities.

Working Professionals in Tech: Upskilling for future-proof roles in AI, cloud, and automation.

Education: 3–4 year STEM degree (B.Tech, B.Sc, MCA, etc.)

Minimum Marks: 50% in graduation

Experience: Preferred 1+ years of industry experience; however, qualified freshers who have prior courses done in AI or ML may apply

Prerequisites: Programming experience required, via academics, work, or projects

Screening: Mandatory pre-screening test to assess readiness for the program

Application Process

Your First Step to the India’s Most Career-Defining AI Certification.

Apply Now
Step 1

Apply Online

Submit your application form with academic and professional details.

Step 2

Take the Pre-Screening Test

Attempt a short test to assess your foundational understanding of AI, cloud, and programming.

Step 3

Profile Review & Shortlisting

Your application and test results will be reviewed by the admissions panel.

Step 4

Interview (If Applicable)

Shortlisted candidates may be invited for a 1:1 interview to evaluate fit and motivation.

Step 5

Receive Admission Offer

Selected candidates will receive an official offer letter from the admissions team.

Step 6

Confirm Your Seat

Block your seat by completing the enrollment process and payment.

Step 7

Graduate with IIT Roorkee Certification

Upon successful completion of the 9-month journey, receive your Advanced Certification in AI Engineering and AIOps from IIT Roorkee.

From Code to Cloud: What You’ll Truly Master Here

Build full-stack GenAI systems

Master the complete development cycle of GenAI applications using LLMs, LangChain, RAG, and sophisticated agent orchestration - moving beyond basic notebooks to production-ready APIs.

Master AI pipelines

Learn to deploy, version, monitor, and retrain models using industry-standard tools like Docker, Kubernetes, and advanced MLOps workflows for seamless production environments.

Design enterprise-ready agents

Create automated workflow solutions using cutting-edge frameworks including AutoGen, Crew AI, and n8n, bringing true intelligence to business processes.

Navigate cloud platforms

Gain expertise in architecting, containerizing, and deploying AI solutions across major cloud platforms - AWS, Azure, and GCP.

Secure and scale AI

Implement robust systems with rollback capabilities, comprehensive observability, security measures, and compliance protocols for enterprise-grade AI deployments.

Own the AI engineering lifecycle

Master the complete spectrum from foundational machine learning to advanced multimodal GenAI and agentic architectures, becoming a full-stack AI engineering professional.

Tools & Platforms
You’ll Master

Apply Now

Tools & Platforms You’ll Master

Cloud
ML/DL
Orchestration
GenAI/Agents
Pipelines
Security & DevOps

Already in AI or Data Science? This Is How You Level Up

Data Scientist
Machine Learning Engineer
Data Engineer
MLOps Engineer
Data Analyst
Business Intelligence Developer
AI Engineer
Cloud Engineer
Data Analyst
Role Now
Data Scientist
Salary Today
4-12 LPA
Role Upgraded
AI Engineer with GenAI specialization
Earning Potential
18 - 35 LPA
Current Skills
Python, R, SQL, Basic Statistics, Data Visualization
Upgraded Skills (From IIT Roorkee Program)
LLM fine-tuning, Prompt Engineering, LangChain, AutoGen, CrewAI, Agentic Workflow Design, RAG, Vector Databases
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System Engineer
Role Now
Machine Learning Engineer
Salary Today
5 - 10 LPA
Role Upgraded
Generative AI Engineer
Earning Potential
20 - 40 LPA
Current Skills
Python, Basic TensorFlow/PyTorch, Simple Model Training
Upgraded Skills (From IIT Roorkee Program)
Advanced GenAI, LLMs (GPT-4, Cohere), Hugging Face, Diffusion Models, GANs, VAEs, Cloud ML Platforms (AWS SageMaker, Azure ML)
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Product Manager
Role Now
Data Engineer
Salary Today
4 -8 LPA
Role Upgraded
AI Data Engineer
Earning Potential
15 - 25 LPA
Current Skills
SQL, Basic ETL, Python, Simple Data Pipelines
Upgraded Skills (From IIT Roorkee Program)
Unstructured Data Processing, AI-driven ETL, Real-time Data Streaming, Cloud-native Pipelines (AWS, Azure, GCP), Vector Databases
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Production Engineer
Role Now
MLOps Engineer
Salary Today
6 - 10 LPA
Role Upgraded
AI Systems Engineer with AIOps
Earning Potential
22 - 45 LPA
Current Skills
Basic Docker, Simple CI/CD, Model Deployment
Upgraded Skills (From IIT Roorkee Program)
AIOps, Multi-agent Orchestration, Advanced MLOps, Kubernetes, Prometheus, Grafana, Seldon, Evidently AI, Cloud Security
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Research Engineer
Role Now
Data Analyst
Salary Today
4 - 8 LPA
Role Upgraded
AI Product Analyst
Earning Potential
18 - 30 LPA
Current Skills
SQL, Excel, Basic Python, Tableau/PowerBI
Upgraded Skills (From IIT Roorkee Program)
Comprehensive AI/ML understanding, Business Impact Analysis, Basic GenAI applications, Data-driven insights with AI tools
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Maintenance Engineer
Role Now
Business Intelligence Developer
Salary Today
4 - 7 LPA
Role Upgraded
Cloud AI Solutions Developer
Earning Potential
20 - 35 LPA
Current Skills
SQL, Basic ETL, Power BI/Tableau, Data Visualization
Upgraded Skills (From IIT Roorkee Program)
AI/ML integration, Cloud platforms, Basic system design, Performance optimization, Serverless AI
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Industrial Engineer
Role Now
AI Engineer
Salary Today
5 - 12 LPA
Role Upgraded
Agentic AI Engineer
Earning Potential
25 - 50 LPA
Current Skills
Python, Basic ML Algorithms, Simple Neural Networks
Upgraded Skills (From IIT Roorkee Program)
Advanced Agentic AI, Multi-agent Systems, Semantic Kernel, Knowledge Base Integration, Production AI Deployment
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Quality Engineer
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Cloud Engineer
Salary Today
4 - 8 LPA
Role Upgraded
Cloud AI Engineer
Earning Potential
25 - 45 LPA
Current Skills
Basic AWS/Azure, Simple Deployments, Basic Networking
Upgraded Skills (From IIT Roorkee Program)
Advanced Cloud AI Services, Serverless AI, Auto-scaling ML Infrastructure, Cost Optimization, AI-native cloud architecture
Top Recruiters
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Haven’t Gotten Your Hands Dirty Yet? There’s Never Been a Better Time

AI Engineer
Machine Learning Engineer
Data Scientist
AIOps Engineer
Data Analyst
AI Product Analyst
Cloud Engineer
Generative AI Engineer
Job Role
AI Engineer
Starting Salary (₹ LPA)
12-30 LPA
Skills Required
Advanced GenAI, LLMs (GPT-4, Cohere), Hugging Face, Diffusion Models, GANs, VAEs, Cloud ML Platforms (AWS SageMaker, Azure ML)
Companies hiring for this role
GoogleMicrosoft_logoamazonDeloitte.svgInfosys_logo
Job Role
Machine Learning Engineer
Starting Salary (₹ LPA)
12-28 LPA
Skills Required
Python, Basic TensorFlow/PyTorch, Model Training, Data Preprocessing, Feature Engineering, Model Evaluation
Companies hiring for this role
GoogleMicrosoft_logoamazonDeloitte.svgInfosys_logo
Job Role
Data Scientist
Starting Salary (₹ LPA)
12-22 LPA
Skills Required
Statistical Analysis, Python, R, SQL, Data Visualization, Machine Learning fundamentals
Companies hiring for this role
GoogleMicrosoft_logoamazonDeloitte.svgInfosys_logo
Job Role
AIOps Engineer
Starting Salary (₹ LPA)
15-35 LPA
Skills Required
Basic Docker, Simple CI/CD, Model Deployment, Kubernetes basics, Prometheus, Grafana, Cloud platforms
Companies hiring for this role
GoogleMicrosoft_logoamazonDeloitte.svgInfosys_logo
Job Role
Data Analyst
Starting Salary (₹ LPA)
12-18 LPA
Skills Required
SQL, Excel, Basic Python, Tableau/PowerBI, Statistical Analysis, Data Visualization
Companies hiring for this role
GoogleMicrosoft_logoamazonDeloitte.svgInfosys_logo
Job Role
AI Product Analyst
Starting Salary (₹ LPA)
15-30 LPA
Skills Required
Comprehensive AI/ML understanding, Business Impact Analysis, Data-driven insights, Product Management basics
Companies hiring for this role
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Job Role
Cloud AI Engineer
Starting Salary (₹ LPA)
18-35 LPA
Skills Required
Basic AWS/Azure, Simple Deployments, Cloud Security, Serverless AI, Auto-scaling ML Infrastructure
Companies hiring for this role
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Job Role
Generative AI Engineer
Starting Salary (₹ LPA)
12-30 LPA
Skills Required
Advanced GenAI, LLMs (GPT-4, Cohere), Hugging Face, Diffusion Models, GANs, VAEs, Cloud ML Platforms (AWS SageMaker, Azure ML)
Companies hiring for this role
GoogleMicrosoft_logoamazonDeloitte.svgInfosys_logo

Step Into the AI Roles Enterprises Are Hiring For

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Learn from the Futurense
Leadership Council

Get industry insights and mentorship directly from an exclusive community of AI experts and CXOs from MAANG companies and Fortune 500s. 
futurense leadership council
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ashish dabas
Ashish Dabas
Vice
President
nitin srivastava
Nitin Srivastava
Data & Analytics
India Lead
anupam gupta
Anupam Gupta
VP Enterprise Data &
Analytics
muthumari s
Muthumari S
Global Head of Data &
AI Studio
anand das
Anand Das
Chief digital & AI Officer: Engineering
a v rahul
A V Rahul
Director,
Analytics
madhu hosadurga
Madhu Hosadurga
Global Vice President,
Enterprise AI
aditya khandekar
Aditya Khandekar
President
‍
pankaj srivastava
Pankaj Srivastava
Partner
‍
satyam mohanty
Satyakam Mohanty
Founder & Managing
Partner
sharmistha chaterjee
Sharmistha Chaterjee
Vice President - Data and AI Shared Capabilities
anirban nandi
Anirban Nandi
Head of AI Products & Analytics (Vice President)
monica s pirgal
Monica S Pirgal
Managing Director &Site head - Board member
sumon mal
Sumon Mal
Head of
Engineering
pankaj rai
Pankaj Rai
Group Chief Data and
Analytics Officer
vishal nagpal
Vishal Nagpal
Data Science and
Analytics Leader
Tushar sahu
Tushar Sahu
Director
Engineering
Ishu jain
Ishu Jain
Head of
Analytics
kaushik das
Kaushik Das
Managing
Director
shrisha ray
Shrisha Ray
Director of
Engineering
divesh singla
Divesh Singla
Head  APAC & Managing Director India & Philippines
Ruchika singh
Ruchika Singh
Director , Data Science
& Insights
arvind balasundaram
Arvind Balasundram
Executive Director, Commercial Insights & Analytics
Indrani goswami
Indrani Goswami
Director of
Analytics
Saurabh kumar
Saurabh Kumar
Director - Data
Engineering
saurabh agarwal
Saurabh Agarwal
Chief Executive
Officer
supriya dhanda
Supria Dhanda
Co-Founder & Managing
Partner
ankit mogra
Ankit Mogra
Director - BI &
Analytics
srini oduru
Srini Oduru
Head of IT Delivery and Operations
alok tiwari
Alok Tiwari
Vice President -
Data
akshay kumar
Akshay Kumar
Research & Analytics
Leader
deepa mahesh
Deepa Mahesh
Head of Strategy & Operations, Board Member
swati jain
Swati Jain
Partner - Digital, AI &
Analytics
krithika muthukrishnan
Krithika Muthukrishnan
Chief Data Science
Officer
tushar chahal
Tushar Chahal
Chief Technology
Officer
sulabh jain
Sulabh Jain
Chief Analytics
Officer
aneel kumar
Aneel kumar
Global Chapter Leader - ICSS, DD&T
arpit agarwal
Arpit Agarwal
Head of Data Science and Analytics
bhairav m
Bhairav M
Senior Manager Data Science and Product Management
saurabh agarwal
Saurabh Agarwal
Chief Executive
Officer
supriya dhanda
Supria Dhanda
Co-Founder & Managing
Partner
ankit mogra
Ankit Mogra
Director - BI &
Analytics
pankaj rai
Pankaj Rai
Group Chief Data and
Analytics Officer
Tushar sahu
Tushar Sahu
Director
Engineering
vishal nagpal
Vishal Nagpal
Data Science and
Analytics Leader
Ishu jain
Ishu Jain
Head of
Analytics
kaushik das
Kaushik Das
Managing
Director
shrisha ray
Shrisha Ray
Director of
Engineering
divesh singla
Divesh Singla
Head  APAC & Managing Director India & Philippines
Ruchika singh
Ruchika Singh
Director , Data Science
& Insights
arvind balasundaram
Arvind Balasundram
Executive Director, Commercial Insights & Analytics
Indrani goswami
Indrani Goswami
Director of
Analytics
Saurabh kumar
Saurabh Kumar
Director - Data
Engineering
saurabh agarwal
Saurabh Agarwal
Chief Executive
Officer
supriya dhanda
Supria Dhanda
Co-Founder & Managing
Partner
ankit mogra
Ankit Mogra
Director - BI &
Analytics
ashish dabas
Ashish Dabas
Vice
President
nitin srivastava
Nitin Srivastava
Data & Analytics
India Lead
anupam gupta
Anupam Gupta
VP Enterprise Data and
Analytics
muthumari s
Muthumari S
Global Head of Data &
AI Studio
anand das
Anand Das
Chief digital & AI Officer: Engineering
aditya khandekar
Aditya Khandekar
President
‍
satyam mohanty
Satyakam Mohanty
Founder & Managing
Partner
sharmistha chaterjee
Sharmistha Chaterjee
Vice President - Data and AI Shared Capabilities
anirban nandi
Anirban Nandi
Head of AI Products & Analytics (Vice President)
monica s pirgal
Monica S Pirgal
Managing Director &Site head - Board member
sumon mal
Sumon Mal
Head of
Engineering
srini oduru
Srini Oduru
Head of IT Delivery and Operations
alok tiwari
Alok Tiwari
Vice President -
Data
akshay kumar
Akshay Kumar
Research & Analytics
Leader
deepa mahesh
Deepa Mahesh
Head of Strategy & Operations, Board Member
swati jain
Swati Jain
Partner - Digital, AI &
Analytics
krithika muthukrishnan
Krithika Muthukrishnan
Chief Data Science
Officer
tushar chahal
Tushar Chahal
Chief Technology
Officer
sulabh jain
Sulabh Jain
Chief Analytics
Officer
aneel kumar
Aneel kumar
Global Chapter Leader - ICSS, DD&T
arpit agarwal
Arpit Agarwal
Head of Data Science and Analytics
bhairav m
Bhairav M
Senior Manager Data Science and Product Management
a v rahul
A V Rahul
Director,
Analytics
madhu hosadurga
Madhu Hosadurga
Global Vice President,
Enterprise AI
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  • What is the name of the certification program, and which IIT offers it?

    The program is called "Advanced PG Certificate Program in AI Engineering on Cloud and AIOps".
    ‍
    It is offered by , one of India’s top-tier IITs.

  • What are the eligibility criteria for this program, and who should ideally enroll?

    This program is ideal for candidates with a solid foundation in mathematics and programming, along with a
    strong interest in technology. Applicants must meet the following eligibility criteria:

    • A 3 to 4-year undergraduate degree in a STEM field (such as BSc or BTech
    • Exposure to programming and computing, either through academic coursework or professional/project experienc
    • A minimum of 50% aggregate marks in their undergraduate program
    • While 1 year of work experience is preferred, well-qualified fresh graduates are also encouraged to apply

  • What is the program's fee, and what financing options are available?

    The program fee is ₹1.25 lakhs + 18% GST.

    Financial options include loans available for 6-month and 12-month terms with a minimal interest rate.

  • Is this course suitable for non technical professionals?

    The curriculum requires significant technical engagement and is best suited for individuals with some technical aptitude or willingness to develop programming skills. However, the progressive structure allows determined learners to build competency gradually.

  • Why is this program considered unique compared to other certifications in AI?

    First program for AI Engineering & AI Ops tailored for developers, data engineers, and MLOps professionals with a strong focus on production-ready AI like MLOps, RAG and secure cloud deployment. The curriculum is taught by IIT Faculty and industry experts with a hands-on, project based
    learning approach where students will get a chance to master 15+ modern tools (LangChain, LLaMA Index, n8n, Crew AI, Kubernetes, etc.).

  • How does this program align with the rapid adoption of generative AI bycompanies like Infosys, TCS, and startups?

    Enterprises and startups are rapidly embedding GenAI and agent-based automation into their cloud workflows. This program trains you on exactly those production-grade stacks including RAG pipelines, multi-agent orchestration, and AIOps which are being deployed across GenAI labs, innovation hubs, and delivery centers in Tier-2 cities and global tech markets.

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