AI Solutions Engineering
The program closes by turning engineering skill into client-ready delivery — gathering requirements, architecting AI solutions and shipping a complete, demonstrable system from problem to production.
What You Will Learn
A detailed, industry-aligned breakdown of every topic covered in this module.
- Client discovery and requirement gathering
- Workflow mapping
- AI solution architecture
- Data and system integration
- API integration
- Security and PII handling
- Solution design and prototyping
- UAT and deployment
- Cost estimation and technical documentation
- Architecture diagrams and client demonstrations
Tools You Will Use
Hands-on time with the same tools used in professional AI engineering and production ML workflows.
Python
Core programming language used across every module, from scripting to AI model development.
FastAPI
Modern Python web framework used to build production-grade backend and AI-serving APIs.
REST APIs
The standard interface pattern used to connect applications, services and AI models.
PostgreSQL
Relational database used to store structured application and AI system data.
LLM APIs
Large language model APIs used to integrate generative AI capabilities into applications.
MCP
Model Context Protocol used to connect AI agents and LLMs to external tools and data sources.
Docker
Containerization platform used to package and deploy applications and AI services consistently.
Cloud Platform
Cloud infrastructure used to deploy, scale and monitor AI systems in production.
GitHub
Code hosting and collaboration platform used for version control, CI/CD and portfolio building.
Hands-On Labs
Production-style AI engineering lab scenarios, built using the same stack real AI teams ship with.
Run a client-discovery exercise and translate requirements into a workflow map.
Design an AI solution architecture with data, system and API integration.
Prototype a client-ready AI solution with proper security and PII handling.
Run UAT on a prototyped solution and prepare it for deployment.
Produce architecture diagrams, technical documentation and a live client demonstration.
Assessment
Knowledge Assessment
Quiz covering solution architecture patterns, integration approaches and UAT/deployment practices.
Practical Evaluation
Students must scope, architect, prototype and demonstrate a complete client-ready AI solution end to end.
Projects
Industry-style deliverables added directly to your project portfolio.
End-to-End AI Solution (Capstone)
Design and build a complete AI solution from requirements to deployment and live demonstration.
What This Module Builds
Students learn to gather requirements, design AI solution architectures, integrate systems and APIs, and prototype and deploy client-ready AI solutions with proper documentation and demonstration.