AI-Native Software Engineering Training Course
AI-Native Software Engineering Training Course is designed to equip software professionals with the skills required to build, deploy, and manage intelligent software systems where Artificial Intelligence is embedded at the core of the engineering lifecycle.
Course Overview
AI-Native Software Engineering Training Course
Introduction
AI-Native Software Engineering Training Course is designed to equip software professionals with the skills required to build, deploy, and manage intelligent software systems where Artificial Intelligence is embedded at the core of the engineering lifecycle. The course focuses on AI-driven development, generative AI, large language models (LLMs), autonomous coding assistants, AI-powered architecture, intelligent automation, cloud-native engineering, DevSecOps, and modern software delivery practices. Participants learn how AI transforms traditional software engineering into an adaptive, data-driven, and autonomous discipline.
Through practical labs, real-world projects, and industry case studies, learners explore how to design AI-native applications, intelligent APIs, autonomous workflows, AI-assisted coding environments, and scalable enterprise solutions. The programme integrates emerging technologies such as Generative AI, Retrieval-Augmented Generation (RAG), AI agents, MLOps, prompt engineering, responsible AI, and intelligent software architecture to prepare engineers for the future of software innovation.
Course Duration
5 days
Course Objectives
By the end of this course, participants will be able to:
- Understand the principles of AI-Native Software Engineering and intelligent application development.
- Design software systems using AI-first architecture and autonomous engineering practices.
- Apply Generative AI tools to accelerate software development workflows.
- Develop applications powered by Large Language Models (LLMs).
- Build intelligent software solutions using AI agents and autonomous workflows.
- Implement AI-assisted coding, testing, debugging, and optimisation techniques.
- Integrate Machine Learning models into modern software applications.
- Apply Prompt Engineering and Context Engineering for enterprise AI solutions.
- Design scalable Cloud-Native and AI-Native application architectures.
- Implement secure AI Software Development Lifecycle (AI-SDLC) practices.
- Apply Responsible AI, governance, and ethical AI engineering principles.
- Use MLOps and DevOps automation for AI-powered software delivery.
- Create innovative business solutions using AI-driven digital transformation strategies.
Target Audience
- Software Engineers and Developers
- Full Stack Application Developers
- AI and Machine Learning Engineers
- Cloud and DevOps Engineers
- Software Architects and Technical Leads
- Product Managers and Technology Managers
- Data Engineers and Analytics Professionals
- IT Consultants and Digital Transformation Specialists
Course Modules
Module 1: Foundations of AI-Native Software Engineering
- Understanding the evolution from traditional software engineering to AI-native development
- Principles of AI-first application design
- AI-native architecture patterns and engineering practices
- Role of Generative AI in modern software teams
- Case Study: How global technology companies use AI coding assistants to improve developer productivity
Module 2: Generative AI for Software Development
- Introduction to Generative AI models and developer ecosystems
- Using AI coding assistants for software creation
- Automated code generation, refactoring, and documentation
- Prompt engineering techniques for developers
- Case Study: Building applications faster using AI pair-programming tools
Module 3: Large Language Models (LLMs) for Developers
- Understanding LLM architecture and capabilities
- Integrating LLM APIs into software applications
- Building conversational and intelligent applications
- Fine-tuning and adapting AI models for business needs
- Case Study: Developing an enterprise AI customer support assistant
Module 4: AI-Native Application Architecture
- Designing scalable AI-powered software systems
- Microservices architecture for AI applications
- AI APIs, intelligent services, and automation layers
- Event-driven architecture for autonomous applications
- Case Study: Designing an AI-powered financial services platform
Module 5: AI Agents and Autonomous Workflows
- Understanding AI agents and autonomous software systems
- Building multi-agent application workflows
- Agent orchestration and decision-making systems
- Integrating tools, APIs, and external knowledge sources
- Case Study: Creating an autonomous business process automation solution
Module 6: AI Software Development Lifecycle (AI-SDLC)
- AI-assisted planning, coding, testing, and deployment
- Automated software quality assurance using AI
- Intelligent debugging and performance optimisation
- Security practices for AI-enabled applications
- Case Study: Implementing an AI-enhanced DevSecOps pipeline
Module 7: Cloud-Native AI Engineering and MLOps
- Deploying AI applications on cloud platforms
- AI infrastructure and scalable computing environments
- Model deployment, monitoring, and lifecycle management
- Continuous Integration and Continuous Deployment (CI/CD) for AI
- Case Study: Managing production-scale AI applications in the cloud
Module 8: Responsible AI and Future Software Engineering
- AI governance, security, and compliance frameworks
- Explainable AI and trustworthy software systems
- Ethical considerations in AI engineering
- Future trends in autonomous software development
- Case Study: Designing a responsible AI solution for healthcare and public services
Training Methodology
- Interactive lectures and presentations.
- Group discussions and brainstorming sessions.
- Hands-on exercises using real-world datasets.
- Role-playing and scenario-based simulations.
- Analysis of case studies to bridge theory and practice.
- Peer-to-peer learning and networking.
- Expert-led Q&A sessions.
- Continuous feedback and personalized guidance.
Register as a group from 3 participants for a Discount
Send us an email: info@datastatresearch.org or call +254724527104
Certification
Upon successful completion of this training, participants will be issued with a globally- recognized certificate.
Tailor-Made Course
We also offer tailor-made courses based on your needs.
Key Notes
a. The participant must be conversant with English.
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
c. Course duration is flexible and the contents can be modified to fit any number of days.
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
e. One-year post-training support Consultation and Coaching provided after the course.
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.