Generative AI Course for Business Leaders and Senior Management in Gurgaon
Kickstart your carrer in AI with certification in Generative AI Engineer course, that will help in shaping the carrer to the current industry needs that need automation using intelligent workflows like Zapier, n8n cloud, Azure Open AI, ChatGPT, RAG Microsoft AutoGen ,LangGraph & others in every domain & sphere of the industry that will allow organizations to boost decision making & also thrive business growth with improved customer satisfaction.
- Self-paced videos & training materials with archived session recordings
- Success Aimers targets industry employers to gain visibility
- Real-World industry scenarios with projects implementation support
- Course Duration - 3 months
- Get Trained from Industry Experts
Train using realtime course materials using online portals & trainer experience to get a personalized teaching experience
Trained through leading industry professionals to get realtime project exposure
Gain professionals insights through leading industry experts across domains
24/7 Q&A support designed to address training needs
Gen AI for Business Leaders Course Overview
Kick start your career in AI with certification in Generative AI Engineer course, that will help in shaping the career to the current industry needs that need automation using intelligent Agentic AI workflow like n8n, Microsoft Auto Gen, Lang Graph and others in every domain and sphere of the industry that will allow organizations to boost decision making and also thrive business growth with improved customer satisfaction.
- Self-paced videos & training materials with archived session recordings
- Success Aimers targets industry employers to gain visibility
- Industry-paced training with real time scenarios using Agentic AI frameworks/tools (n8n, Microsoft Auto Gen, Lang chain, Lang Graph, AWS Bedrock, Azure Open AI and others) for intelligent automation.
- Real-World industry scenarios with projects implementation support
- Live Virtual classes heading by top industry experts along with project implementation
- 24/7 Q&A support sessions
- Job Interview preparation and use cases
Explain Gen AI Engineer?
AI Engineers automate business processes via AI development frameworks (Agentic AI) like n8n, Microsoft Auto Gen, Lang chain, Lang Graph, AWS Bedrock, Azure Open AI and others for intelligent automation that will allow organizations to boost decision making and also thrive business growth with improved customer satisfaction. AI engineers build optimized workflows, deployment and maintain the AI development life cycle to deliver high-précise solutions to the clients using Agentic AI frameworks/workflow builders.
Role of Gen AI Engineer?
Generative AI Engineers manages the end-to-end AI development life cycle using Agentic AI frameworks and intelligent workflow strategies.
Responsibilities include:
- Agentic AI Tools Adoption & automation using patterns like RAG, Tools, Reflection and others
- Continuously exploring new AI based frameworks/tools to build optimized workflows
- Deploying AI model within cloud infrastructure securely & seamlessly.
- Develop and Design AI workflows that meets business needs with automation scripts
- Success Aimers helps aspiring AI professionals to build, deploy, manage these Agents effectively seamlessly and to scale as per business needs.
Who should opt for Gen AI Engineer course?
Gen AI course accelerates/boost carrier in AI & technology-driven organizations
- AI & Application Engineers – Develop Agentic AI workflows to automate business processes.
- Data Engineers – Implementing Data Pipelines using AI
- AI Developers – Automated workflows using Agentic AI frameworks/tools
- AI Leaders – Leading AI Governance and initiative within enterprise.
- Cloud and Infrastructure Engineers – Deploying AI developed applications seamlessly and effectively.
Prerequisites of Gen AI Engineer Course?
Prerequisites required for the Generative AI Engineer Certification Course
- Below is the prerequisite to become a successful AI Professional:
- High School Diploma or a undergraduate degree
- Python Programming language
- IT Foundational Knowledge along wit Data Science and Software Development skills
- Knowledge of Cloud Computing Platforms like AWS, AZURE and GCP will be an added advantage.
Kind of Job Placement/Offers after Gen AI Engineer Certification Course?
Job Career Path in Generative AI and N8N Automation
- Generative AI Engineer – Develop & Deploy AI developed solutions/applications using agentic frameworks/tools
- AI/ML Engineer – Build context-aware, Enterprise ready AI systems.
- AI Automation Engineer – Design, Developed and build automated workflows to drive key business processes/decisions
- Cloud AI Engineer / AI Cloud Architect – Deploy AI Solutions seamlessly and effectively across enterprises with AI Governance strategy
- AI Model Deployment Engineer – Automate Model Deployment and AI Life cycle management via ML Ops pipeline and Agentic AI Frameworks
| Training Options | Weekdays (Mon-Fri) | Weekends (Sat-Sun) | Fast Track |
|---|---|---|---|
| Duration of Course | 2 months | 3 months | 15 days |
| Hours / day | 1-2 hours | 2-3 hours | 5 hours |
| Mode of Training | Offline / Online | Offline / Online | Offline / Online |
Gen AI Certification Course Curriculum for Business Leaders
Start your career in AI with certification in Generative AI Engineer course, that will help in shaping the career to the current industry needs that need automation using intelligent workflows like n8n, Microsoft Auto Gen, Lang Graph and others in every domain and sphere of the industry that will allow organizations to boost decision making and also thrive business growth with improved customer satisfaction.
Module 1 - Introduction to data analysis Concepts and Workflows using Python
Objective: Master data preprocessing and exploratory data analysis techniques essential for effectively training Generative AI models.
Topics Covered :
Performing data analysis, cleaning, and preprocessing with Pandas and NumPy
Creating impactful data visualizations using BI tools
Conducting feature selection and engineering to enhance AI model performance
Embedding machine learning models into dashboards and reports for advanced analytics insights
Real-World Use Cases :
Clinical Data Analysis: Examine healthcare facility data to identify trends and insights in patient outcomes.
Healthcare Finance Analytics: Analyze public finance data to inform strategic budget allocations in the healthcare sector.
Experiential Learning Labs:
Data Cleaning & Preprocessing: Work with a real-world health insurance claims dataset to prepare it for analysis.
Exploratory Data Analysis (EDA): Analyze public healthcare expenditure data to uncover patterns and insights.
Practical Takeaway Project :Â Perform feature engineering and create data visualizations on clinical trial or patient datasets to evaluate the impact of specific treatments.
Module 2: Generative AI Essentials for Senior Management
Focus Areas :
Generative AI Fundamentals: Core concepts, techniques, and transformative potential.
Strategic Impact of GenAI: How Generative AI is reshaping industries such as health insurance, public finance, healthcare, and government policy.
Executive Use Cases: Applications in healthcare claims generation, fraud detection, policy automation, and decision-making tools for financial management.
Strategic Use Cases:
Synthetic Health Data Generation: Creating simulated healthcare datasets to model insurance risk.
Personalized Policy Recommendations: Leveraging GenAI to optimize public finance strategies and decision-making.
Module 3 - AI Project Management for Business Leaders
Focus Areas :
Driving Generative AI Initiatives Across the Organization
Applying AI-Specific Project Management Frameworks
Leading Cross-Functional Teams on AI-Driven Projects
Setting AI Project Scope, Timelines, and Stakeholder Alignment
Strategic Use Cases:
AI Project Management in Healthcare: Overseeing the development of AI-based healthcare reimbursement systems.
Automating Public Finance Operations: Managing AI deployments to streamline financial report generation.
Experiential Learning Labs: Design and plan a Generative AI implementation project for a health insurance organization or a public health initiative.
Module 4: Generative AI Project Costing and ROI Analysis
Focus Areas :
Assessing AI Investments: Understanding the financial commitment required for AI technologies.
Estimating Costs: Evaluating infrastructure, cloud, and development expenses.
ROI Analysis: Measuring the return on investment of AI systems in public health and finance.
Budget Planning: Allocating resources for data acquisition, storage, and computational needs in AI projects.
Strategic Use Cases:
Fraud Detection ROI: Evaluating the cost-benefit of deploying Generative AI for fraud detection in health insurance.
Automated Claims Impact: Estimating the financial gains from implementing AI-driven claims processing in public health insurance.
Experiential Learning Labs: Plan the budget for a Generative AI project focused on clinical health data processing.
Module 5 - Risk Assessment and Strategic Mitigation for AI Initiatives
Focus Areas :
AI Deployment Risk Identification: Recognizing potential risks, including data privacy, ethics, and regulatory compliance.
Risk Mitigation Strategies: Implementing measures to manage risks in large-scale Generative AI projects.
Legal & Ethical Compliance: Addressing healthcare regulations and standards (e.g., Clinical Establishments, MCI).
Bias and Security Management: Preventing model bias and ensuring data security in finance and healthcare applications.
Strategic Use Cases:
Clinical AI Risk Management: Implementing strategies to safely deploy AI-based clinical decision support systems.
Bias Mitigation in Public Policy AI: Reducing risks of bias in Generative AI algorithms for public policy data analysis.
Experiential Learning Labs: Conduct a risk assessment for implementing a Generative AI–based financial advisory system within a government-run health insurance program.
Module 6 - Innovating Products through Generative AI and Design Thinking
Focus Areas :
AI Solution Conceptualization: Ideating and designing AI-driven products in healthcare, insurance, and public policy.
Agile AI Product Design: Applying agile methodologies to accelerate AI product development.
AI Lifecycle Management: Managing AI models end-to-end with continuous learning and improvement.
Strategic Use Cases:
Health Insurance Automation: Designing AI-driven tools to automate claims processing.
Public Finance Forecasting: Developing AI-based models for accurate financial forecasting and planning.
Experiential Learning Labs: Develop a blueprint for an AI-powered health insurance product, from concept to implementation strategy.
Module 7 - Regulatory and Compliance Guidelines for Generative AI in Healthcare and Finance
Focus Areas :
Regulatory Awareness: Understanding the legal and regulatory landscape for AI deployment.
Compliance Management: Addressing challenges of implementing AI in sensitive sectors such as healthcare and finance.
AI Ethics: Ensuring ethical considerations guide AI-driven decision-making processes.
Auditability & Transparency: Maintaining transparency and audit trails in AI systems for accountability.
Strategic Use Cases:
Healthcare Compliance: Ensuring AI-generated health risk assessments meet regulatory standards.
Public Finance Legal Management: Addressing legal and regulatory challenges when deploying AI for financial planning.
Experiential Learning Labs: Develop a compliance and regulatory plan for implementing a Generative AI product in public healthcare.
Module 8 - Emerging Trends and Responsible Practices in Generative AI
Focus Areas :
Generative AI in Key Sectors: Exploring emerging applications in health insurance, public finance, and healthcare.
Strategic AI Planning: Developing long-term strategies for AI-driven innovation.
AI-Driven Business Models: Leveraging AI to create value and new business opportunities.
Responsible AI: Understanding definitions, principles, and the importance of ethical AI practices.
Ethical Considerations: Addressing bias, fairness, accountability, transparency, and explainability in AI systems.
Case Studies: Examining real-world ethical dilemmas and challenges in AI development and deployment.
Regulatory Overview: Navigating data protection laws and regulations, including GDPR and CCPA.
Strategic Use Cases:
Healthcare Cost Forecasting: Leveraging AI to predict trends in healthcare expenditures.
AI-Powered Policy Recommendations: Developing AI-driven systems to support decision-making and governance in public finance.
Experiential Learning Labs: Develop a strategic roadmap for implementing future Generative AI initiatives in health insurance or public healthcare.
Capstone Project: Building an AI-Powered Health Insurance Solution
Project Outline:
AI System Design: Develop an AI-driven solution for health insurance claims processing.
Automation & Fraud Detection: Automate document analysis, detect fraudulent claims, and provide personalized insurance recommendations.
Strategic Planning: Conduct cost, risk, and ROI analysis; create a comprehensive project plan and draft a government funding proposal.
Expected Outcomes:
Comprehensive Project Blueprint: A detailed design for a Generative AI solution.
Financial and Risk Assessment: Complete cost, risk, and ROI analysis report.
Stakeholder Presentation: An AI-driven product pitch tailored for executive and stakeholder review.
Hours of content
Live Sessions
Software Tools
After completion of this training program you will be able to launch your carrer in the world of AI being certified as Generative AI Certified Professional.
With the AI Certification in-hand you can boost your profile on Linked, Meta, Twitter & other platform to boost your visibility
- Get your certificate upon successful completion of the course.
- Certificates for each course
- Agentic AI
- Gen AI
- RAG
- Gen AI Governance
- Gen AI Guardrails
- Gen AI Project Management
- Gen AI Compliance and Regulatory Frameworks
- Product Development with Gen AI
- Risk Analysis and Mitigation
- Cost Analysis and ROI for GenAI Projects
- GenAI Project Management
- Managing GenAI initiatives
- Design Thinking and Product Development with GenAI
- Future Trends in Generative AI and Responsible AI
45% - 100%
Designed to provide guidance on current interview practices, personality development, soft skills enhancement, and HR-related questions
Receive expert assistance from our placement team to craft your resume and optimize your Job Profile. Learn effective strategies to capture the attention of HR professionals and maximize your chances of getting shortlisted.
Engage in mock interview sessions led by our industry experts to receive continuous, detailed feedback along with a customized improvement plan. Our dedicated support will help refine your skills until your desired job in the industry.
Join interactive sessions with industry professionals to understand the key skills companies seek. Practice solving interview question worksheets designed to improve your readiness and boost your chances of success in interviews
Build meaningful relationships with key decision-makers and open doors to exciting job prospects in Product and Service based partner
Your path to job placement starts immediately after you finish the course with guaranteed interview calls
Why should you choose to pursue a Gen AI/Agentic AI course with Success Aimers?
Success Aimers teaching strategy follow a methodology where in we believe in realtime job scenarios that covers industry use-cases & this will help in building the carrer in the field of AI & also delivers training with help of leading industry experts that helps students to confidently answers questions confidently & excel projects as well while working in a real-world
What is the time frame to become competent as a Gen AI engineer?
To become a successful Gen AI Engineer required 1-2 years of consistent learning with dedicated 3-4 hours on daily basis. But with Success Aimers with the help of leading industry experts & specialized trainers you able to achieve that degree of mastery in 6 months or one year or so and it’s because our curriculum & labs we had formed with hands-on projects.
Missing a live session doesn't impact your training becauase we have the live recorded session that's students can refer later.
Missing a live class won’t prevent you from completing the course. You can conveniently watch the recorded session of any class you miss at your own pace.
What industries lead in Gen AI implementation?
- Manufacturing
Financial Services
Healthcare
E-commerce
Telecommunications
BFSI (Banking, Finance & Insurance)
“Travel Industry
At Success Aimers, we have tied up with 500 + Corporate Partners to support their talent development through online training. Our corporate training programme delivers training based on industry use-cases & focused on ever-evolving tech space.
At Success Aimers, we have tied up with 500 + Corporate Partners to support their talent development through online training. Our corporate training programme delivers training based on industry use-cases & focused on ever-evolving tech space.
How is the Success Aimers Gen AI Certification Course reviewed by learners?
Our Gen AI Engineer Course features a well-designed curriculum frameworks focused on delivering training based on industry needs & aligned on ever-changing evolving needs of today’s workforce due to AI.
Also our training curriculum has been reviewed by alumni & praises the thorough content & real along practical use-cases that we covered during the training. Our program helps working professionals to upgrade their skills & help them grow further in their roles…
Can I attend a demo session before I enroll?
Yes, we offer one-to-one discussion before the training and also schedule one demo session to have a gist of trainer teaching style & also the students have questions around training
programme placements & job growth after training completion.
What batch size do you consider for the course?
On an average we keep 5-10 students in a batch to have a interactive session & this way trainer can focus on each individual instead of having a large group
Do you offer learning content as part of the program?
Students are provided with training content wherein the trainer share the Code Snippets, PPT Materials along with recordings of all the batches
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