AI Engineer Pathway (LLM App Builder)
From Concept to Production. Skills That Work.
Build, deploy, and secure real-world AI applications powered by Large Language Models. This mentor-led pathway is designed for professionals who want to engineer AI systems that operate reliably in production environments, not just prototypes or demos.
Pricing applies to the current cohort only
Investment
Pathway Overview
The AI Engineer Pathway (LLM App Builder) is a structured, hands-on programme focused on practical AI engineering. It takes learners from foundational concepts through to designing, building, deploying, and governing production-grade LLM applications.
Rather than focusing on abstract theory, this pathway reflects how AI is implemented inside modern organisations. You will work with real APIs, real data, real deployment pipelines, and real risk considerations, gaining experience that translates directly into professional roles.
This is a professional skills pathway for those who want to build AI products, integrate them into business systems, and operate them responsibly at scale.
Pathway Structure
Six stages from foundations to capstone project
Engineering Foundations for AI
Python for AI application development, API integration, async processing, and data flows. Practical AI and machine learning concepts for engineers.
LLM Engineering Fundamentals
Prompt engineering for production environments. Working with leading LLM APIs. Token usage, performance optimisation, and cost control. Embeddings and semantic search foundations.
Building LLM Applications
Chatbots, copilots, and internal AI tools. Retrieval-Augmented Generation (RAG) design patterns. Document ingestion from files, databases, and APIs. Orchestration and agent-based workflows.
Backend Engineering and Deployment
API development for AI services. Authentication, rate limiting, logging, and monitoring. Docker and containerised deployments. Cloud deployment aligned with AWS, Azure, and GCP environments.
AI Security, Risk, and Governance
AI threat models and misuse scenarios. Prompt injection and data leakage risks. Secure AI architecture and guardrail implementation. Privacy-aware AI design and GDPR alignment. Monitoring, audit trails, and operational controls.
Capstone Project
End-to-end LLM application build with realistic business use case. Architecture design and technical documentation. GitHub repository and working demo. Portfolio review and career guidance.
Why This Pathway Exists
Most AI courses stop at models or prompts. Real organisations need engineers who can:
Build AI applications end to end
Integrate LLMs with real data and systems
Deploy securely to cloud platforms
Manage risk, privacy, and cost
This pathway was designed to close that gap.
What You Will Learn
Design and engineer LLM-powered applications using modern AI APIs
Apply structured prompt engineering and tool-based workflows
Build Retrieval-Augmented Generation (RAG) systems using vector databases
Develop secure backend services for AI applications
Deploy containerised AI systems to cloud platforms
Apply AI security, privacy, and governance controls
Deliver a complete, portfolio-ready AI engineering project
Portfolio & Evidence
Build real-world projects that demonstrate your capabilities to employers:
End-to-end LLM application with realistic business use case
Architecture design and technical documentation
GitHub repository with working demo
Portfolio-ready project with career guidance
Prerequisites
Basic programming experience in any language
Familiarity with APIs or web concepts is beneficial
No advanced mathematics or academic AI background required
Career Outcomes
This pathway prepares you for high-demand roles in AI engineering
AI Engineer
Avg. Salary:
LLM Application Developer
Avg. Salary:
AI Solutions Engineer
Avg. Salary:
AI Platform Engineer
Avg. Salary:
AI Security and Risk Specialist
Avg. Salary:
Machine Learning Engineer
Avg. Salary:
The Skills Gap We Fill
Organisations need engineers who can build complete AI applications end to end, integrate LLMs with data sources, deploy securely to cloud platforms, and manage cost, privacy, and operational risk. This pathway was created to address those real-world requirements.
How You'll Learn
A structured, mentor-led journey designed for working professionals
Live Online Virtual
Instructor-Led Training
Interactive, instructor-led sessions with real-world examples, discussion, and live Q&A. Learn from anywhere while staying connected to your cohort and mentors.
Includes live sessions, self-study, and project work. Flexible schedule designed for working professionals.
What's Included
Live Instructor-Led Sessions
Real-time interactive classes with expert instructors
Mentor Guidance & Reviews
Personal feedback on your work and career advice
Structured Weekly Progression
Clear milestones and pacing to keep you on track
Practical Projects & Workplace Scenarios
Build a portfolio with industry-relevant examples
Private Learning Community
Connect with peers and expand your network
Flexible Learning Resources
Downloadable materials and guided references
Certification & Role Preparation
Exam prep and career transition support
Progress Tracking & Recognition
Digital badges and completion certificates
Your Weekly Learning Rhythm
Attend live sessions
Practice concepts
Create projects
Get mentor feedback
Have questions about this pathway?
Speak with a learning advisor to find the right fit for your goals.
Tools & Technologies
Exam-Aligned Training
This pathway focuses on building practical AI engineering capability while providing learning aligned with recognised certification objectives and industry standards.
Exam-Aligned Coverage Includes
Microsoft Certified: Azure AI Engineer Associate
Exam Code: AI-102
Alignment with Azure OpenAI services, AI solution design, integration, and operational deployment concepts referenced in published exam objectives.
AWS Certified Machine Learning – Specialty
Conceptual and practical alignment with machine learning engineering, data pipelines, deployment, and monitoring principles within AWS environments.
Google Professional Machine Learning Engineer
Alignment with core machine learning engineering domains and scalable AI deployment practices.
ISO/IEC 42001 – AI Management System
Alignment with AI governance, risk management, lifecycle controls, and responsible AI practices. This programme does not provide ISO certification or auditor credentials.
What This Training Provides
- Practical engineering skills and hands-on experience
- Content mapped to exam objectives and domains
- Portfolio projects demonstrating capability
- Real-world scenarios and production readiness
Certification Exam Coverage
- Content aligned with vendor exam objectives
- Practice scenarios based on exam domains
- Preparation for industry-recognized certifications
- Skills development beyond exam requirements
Exam-aligned training for professional development.
Build skills that organisations rely on and certifications recognise.
Frequently Asked Questions
Everything you need to know about this pathway
This pathway focuses on building practical AI engineering capability. Training content is mapped to publicly available exam blueprints and technical domains from Microsoft Azure AI Engineer (AI-102), AWS Machine Learning Specialty, Google Professional ML Engineer, and ISO/IEC 42001 AI Management System. Xcademia is not an authorised training provider or examination centre. All certification exams are optional and booked directly by delegates with the relevant awarding bodies.
Build AI Systems Ready for Production
If you want more than theory, this pathway is built for you.