Description
Certification Name: Certificate in Artificial Intelligence (AI) Engineer
Course Id: CEAP/Q0001.
Eligibility: Graduation or Equivalent.
Objective: The Certified Artificial Intelligence (AI) Engineer course is designed to equip professionals with the knowledge and skills to design, develop, and deploy AI-based solutions across various industries. The course covers core AI concepts, machine learning algorithms, deep learning, natural language processing (NLP), computer vision, data preprocessing, model training, and deployment.
Duration: Three Month.
How to Enroll and Get Certified in Your Chosen Course:
Step 1: Choose the course you wish to get certified in.
Step 2: Click on the “Enroll Now” button.
Step 3: Proceed with the enrollment process.
Step 4: Enter your billing details and continue to course fee payment.
Step 5: You will be redirected to the payment gateway. Pay the course and exam fee using one of the following methods:
Debit/Credit Card, Wallet, Paytm, Net Banking, UPI, or Google Pay.
Step 6: After successful payment, you will receive your study material login ID and password via email within 48 hours of fee payment.
Step 7: Once you complete the course, take the online examination.
Step 8: Upon passing the examination, you will receive:
• A soft copy (scanned) of your certificate via email within 7 days of examination.
• A hard copy (original with official seal and signature) sent to your address within 45 day of declaration of result.
Step 9: After certification, you will be offered job opportunities aligned with your area of interest.
Online Examination Detail:
Duration- 60 minutes.
No. of Questions- 30. (Multiple Choice Questions).
Maximum Marks- 100, Passing Marks- 40%.
There is no negative marking in this module.
| Marking System: | ||||||
| S.No. | No. of Questions | Marks Each Question | Total Marks | |||
| 1 | 10 | 5 | 50 | |||
| 2 | 5 | 4 | 20 | |||
| 3 | 5 | 3 | 15 | |||
| 4 | 5 | 2 | 10 | |||
| 5 | 5 | 1 | 5 | |||
| 30 | 100 | |||||
| How Students will be Graded: | ||||||
| S.No. | Marks | Grade | ||||
| 1 | 91-100 | O (Outstanding) | ||||
| 2 | 81-90 | A+ (Excellent) | ||||
| 3 | 71-80 | A (Very Good) | ||||
| 4 | 61-70 | B (Good) | ||||
| 5 | 51-60 | C (Average) | ||||
| 6 | 40-50 | P (Pass) | ||||
| 7 | 0-40 | F (Fail) | ||||
Key Benefits of Certification- Earning a professional certification not only validates your skills but also enhances your employability. Here are the major benefits you gain:
Practical, Job-Ready Skills – Our certifications are designed to equip you with real-world, hands-on skills that match current industry demands — helping you become employment-ready from day one.
Lifetime Validity – Your certification is valid for a lifetime — no renewals or expirations. It serves as a permanent proof of your skills and training.
Lifetime Certificate Verification – Employers and institutions can verify your certification anytime through a secure and reliable verification system — adding credibility to your qualifications.
Industry-Aligned Certification –All certifications are developed in consultation with industry experts to ensure that what you learn is current, relevant, and aligned with market needs.
Preferred by Employers – Candidates from ISO-certified institutes are often prioritized by recruiters due to their exposure to standardized, high-quality training.
Free Job Assistance Based on Your Career Interests – Receive personalized job assistance and career guidance in your preferred domain, helping you land the right role faster.
Assessment Modules:
Module 1 – Foundations of AI & Programming: Introduction to artificial intelligence and historical developments, Mathematical foundations (linear algebra, probability, statistics, calculus), Programming for AI (Python basics, data types, functions, libraries), Data structures & algorithms relevant to AI, Data engineering for AI: data acquisition, cleaning, pipelines, Exploratory data analysis and visualization
Module 2 – Machine Learning Algorithms & Techniques: Supervised learning (regression, classification, decision trees, SVMs), Unsupervised learning (clustering, dimensionality reduction, PCA), Ensemble methods and boosting (Random Forest, XGBoost, bagging), Model evaluation, validation and hyper‑parameter tuning, Feature engineering, selection and extraction, Handling imbalanced data, over‑fitting, under‑fitting
Module 3 – Deep Learning & Neural Networks: Basic neural network architecture (layers, activation functions, loss, backpropagation), Convolutional Neural Networks (CNNs) for image data, Recurrent Neural Networks (RNNs), LSTM/GRU for sequential data, Transfer learning and pre‑trained models, Generative models (GANs, autoencoders)
Module 4 – Natural Language Processing, Computer Vision & Multimodal AI: Text preprocessing, tokenisation, embeddings (Word2Vec, GloVe, BERT etc.), Sequence‑to‑sequence models, attention mechanisms and transformers, Computer vision tasks: image classification, object detection, segmentation, Multimodal AI: combining vision, text, audio, Use‑cases and applications in real‑world domains
Module 5 – AI Deployment, MLOps & Cloud Integration: Model deployment strategies (APIs, microservices, edge, mobile), MLOps lifecycle: model versioning, monitoring, governance, Continuous integration/continuous deployment for AI models, Cloud platforms for AI (AWS, Azure, GCP) and serverless AI services, Scalability, performance optimisation, inference latency and cost considerations, Ethical AI and bias mitigation in deployed systems
Module 6 – Emerging Trends, Strategy & Ethical Considerations in AI: Reinforcement learning and autonomous systems, Foundation models and large language models (LLMs), AI for IoT, edge computing, and robotics, AI governance, regulation and responsible AI (bias, fairness, privacy), Business strategy for AI: road‑map, value realisation, change management, Future of AI: quantum AI, explainable AI (XAI), sustainability and social impact
After successful completion of the Certificate in Artificial Intelligence (AI) Engineer course, graduates can pursue high-demand careers focused on developing, deploying, and maintaining AI-driven systems across industries. Below is a detailed overview of career options with roles, responsibilities, and salary ranges in India.
1. AI Engineer / AI Developer
Role Overview:
Designs, develops, and deploys AI models and intelligent systems to solve business problems.
Key Responsibilities:
-
Develop machine learning and deep learning models
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Implement AI algorithms for prediction, classification, and automation
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Preprocess and analyze datasets
-
Deploy AI models into production environments
Salary Range (India):
₹7 LPA – ₹18 LPA
(Senior AI Engineers can earn ₹25 LPA+)
2. Machine Learning (ML) Engineer
Role Overview:
Specializes in designing and training ML models for AI applications.
Key Responsibilities:
-
Build supervised, unsupervised, and reinforcement learning models
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Optimize model performance and accuracy
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Develop pipelines for data ingestion and preprocessing
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Deploy ML models using cloud or on-premise infrastructure
Salary Range (India):
₹7 LPA – ₹16 LPA
3. Data Scientist (AI Focus)
Role Overview:
Analyzes large datasets using AI techniques to extract insights and support decision-making.
Key Responsibilities:
-
Apply AI/ML techniques to solve business problems
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Perform data cleaning, visualization, and feature engineering
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Build predictive and prescriptive models
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Communicate insights to stakeholders
Salary Range (India):
₹6 LPA – ₹15 LPA
4. AI Research Engineer
Role Overview:
Works on developing novel AI algorithms and models for complex problems.
Key Responsibilities:
-
Research and implement advanced AI techniques
-
Experiment with neural networks, NLP, and computer vision models
-
Optimize model efficiency and scalability
-
Publish findings and contribute to AI innovation
Salary Range (India):
₹8 LPA – ₹20 LPA
5. Natural Language Processing (NLP) Engineer
Role Overview:
Builds AI systems that understand and process human language.
Key Responsibilities:
-
Develop chatbots, virtual assistants, and text analytics tools
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Implement NLP algorithms for sentiment analysis, summarization, and translation
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Preprocess and manage text datasets
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Deploy NLP models in production
Salary Range (India):
₹7 LPA – ₹18 LPA
6. Computer Vision Engineer
Role Overview:
Develops AI systems for visual data processing in applications like image recognition, video analytics, and autonomous systems.
Key Responsibilities:
-
Build and train image and video analysis models
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Implement object detection, facial recognition, and segmentation
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Optimize models for real-time applications
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Integrate computer vision solutions into products
Salary Range (India):
₹7 LPA – ₹20 LPA
7. AI Product Developer / AI Solution Architect
Role Overview:
Designs and develops AI-driven products or end-to-end solutions for enterprises.
Key Responsibilities:
-
Identify AI opportunities in products and services
-
Design AI system architecture
-
Coordinate with data, engineering, and business teams
-
Ensure AI model performance and scalability
Salary Range (India):
₹10 LPA – ₹25 LPA+
8. AI Consultant / AI Specialist
Role Overview:
Advises businesses on AI strategy, implementation, and optimization.
Key Responsibilities:
-
Evaluate AI adoption potential
-
Recommend AI technologies and models
-
Assist in deployment and monitoring of AI solutions
-
Train teams on AI tools and processes
Salary Range (India):
₹8 LPA – ₹20 LPA
9. Robotics & Automation Engineer (AI Focus)
Role Overview:
Integrates AI into robotic systems for automation, smart manufacturing, and intelligent devices.
Key Responsibilities:
-
Develop AI-driven control systems
-
Implement computer vision and reinforcement learning for robots
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Optimize automation processes using AI
-
Deploy intelligent systems in production
Salary Range (India):
₹8 LPA – ₹22 LPA
10. Freelance AI Developer / Entrepreneur
Role Overview:
Provides AI solutions, develops AI products, or offers consultancy independently.
Key Responsibilities:
-
Build AI models and applications for clients
-
Provide AI consulting and training
-
Deploy AI solutions across industries
-
Innovate AI-based products and services
Earning Potential (India):
₹10 LPA – ₹35 LPA+ (project-based or product-driven)
Industries Hiring AI Engineers
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IT services and product companies
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Banking, finance, and fintech
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Healthcare and pharmaceuticals
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Retail, e-commerce, and logistics
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Automotive and autonomous systems
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Telecommunications, media, and smart cities
Career Growth Outlook
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High demand due to AI adoption across industries
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Opportunities for global remote roles and research positions
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Clear career path: AI Engineer → Senior AI Engineer → AI Solution Architect → AI Lead / Head of AI
-
Emerging domains include generative AI, AIoT, and autonomous systems
The Certificate in Artificial Intelligence (AI) Engineer equips professionals to design, develop, and deploy intelligent systems, offering high salary potential, cross-industry relevance, and long-term career growth in India.
