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AI for FDE vs Applied AI Engineering
Both are 8-week, live, mentor-led, build-first programs that share the same AI core (LLMs, RAG, agents, evaluation, production). They differ in what they turn you into: one makes you the engineer who builds AI systems, the other the engineer who deploys AI solutions with customers, end to end.
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AI for Forward Deployed Engineers
Deploy AI solutions with customers, end to end.
- Who it's for
- Engineers and solution builders who want the hottest, customer-facing AI role.
- Career direction
- Forward Deployed Engineer · AI Solutions Engineer · AI Solutions Architect · Applied AI Consultant.
Pick this if
- You want to sit with customers and turn a messy problem into a deployed solution
- You enjoy the whole lifecycle: discovery, design, integration, deployment, handover
- You are as interested in solutioning and communication as in code
- You want design-thinking and delivery skills, not just model depth
Pick this to
Applied AI Engineering
Build AI systems, from model to production.
- Who it's for
- Software engineers who want to become AI engineers and build AI deeply.
- Career direction
- AI Engineer · Applied LLM Engineer · ML Engineer · AI Product Engineer.
Pick this if
- You want to go deep on how AI systems are built, end to end
- You want classical ML and deep-learning foundations, not just LLM usage
- You want to build and ship AI features and products yourself
- Your goal is engineering depth over customer-facing delivery
What each program covers
Same AI core, different centre of gravity. A high-level view.
| AI for Forward Deployed Engineers | Applied AI Engineering | |
|---|---|---|
| Who it is for | Engineers moving into customer-facing AI delivery (FDE) | Engineers becoming AI engineers who build systems |
| Career direction | Forward Deployed Engineer, AI Solutions Engineer/Architect | AI Engineer, Applied LLM/ML Engineer |
| The one-line difference | Deploy AI solutions with customers | Build AI systems |
| AI core (LLMs, RAG, agents) | Yes, taught as solution-building tools | Yes, taught in depth including internals |
| Classical ML & deep learning | Light literacy only | Full: regression, trees, CNNs, transformers |
| Design thinking & discovery | Core: framing, discovery, use-case qualification | Light |
| Integration & data plumbing | Core: enterprise APIs, connectors, auth, vector stores | Touched via RAG and production |
| Deploy in customer environments | Core: cloud, VPC, on-prem, security, data residency | Production deployment of your own build |
| Consulting, handover & delivery | Core: stakeholder work, demos, runbooks, enablement | Not a focus |
| Capstone | A bespoke customer solution, defended to a customer panel | A deployed, defended agentic AI product |
| Format | 8 weeks · design-thinking led · 4 weekend masterclasses | 8 weeks · build-first · 6 projects from a 17-project catalogue |
Still weighing it up? Both are 8-week, live, mentor-led, and build-first. Choose FDE if you want to deploy AI with customers; choose Applied AI Engineering if you want to build AI systems deeply.