Flagship guided program · AI/ML Engineer track
A Forward-Deployed Engineer works close to the problem: understanding what a team needs, directing AI and coding tools at the work, and shipping a production feature that can survive contact with real users. This program is for people who already know software development and want to make that experience useful in AI-enabled delivery.
Apply / book a callWe do not start by asking you to memorise another stack. The program starts with AI/ML fluency and the practitioner skill of directing AI coding and agentic tools. Traditional application and delivery work comes next, as the layer where that fluency becomes useful.
You are not expected to be new to programming. You should be comfortable reading and changing software, working with a repository, and reasoning about a deployed service. You may come from backend, DevOps, QA, or another technical role; the program makes the common AI foundation explicit before the FDE tail becomes hands-on.
Program shape
Plan for roughly 4–6 months part-time. The exact pace is shaped around your starting point and the depth of the capstone.
Stage 1
Understand what models compute, how cost and latency shape a product, and how to direct AI coding tools with precise prompts, context, and machine-usable output.
Stage 2
Move from impressive demos to systems that can retrieve, cite, and explain the data behind an answer, while recognising when retrieval is failing.
Stage 3
Use tools, protocols, memory, and orchestration to make an AI system do useful work without treating autonomy as a substitute for engineering judgment.
Stage 4
Evaluate outputs, detect hallucinations, apply guardrails, and instrument AI systems so a team can understand and improve them after release.
Stage 5
Apply the spine to unfamiliar repositories: direct AI coding tools at real work, integrate a feature, and reason through what changes when it misbehaves in production.
Stage 6
Finish a grounded, tool-using feature and turn the engineering decisions, trade-offs, and results into evidence you can discuss with a hiring team or client.
A polished prompt demo is not the finish line. You will practise moving through an existing codebase, making a bounded change, validating it, and explaining what you chose not to automate.
The role sits close to users and stakeholders. Scoping ambiguity, asking the right question, communicating trade-offs, and turning feedback into the next useful slice are part of the engineering work.
The capstone gives you a concrete system and a decision record to discuss. We do not publish invented placement numbers or pretend that one project guarantees a job.
System design, debugging, behavioural discussion, and explaining an AI-assisted decision are woven into the work rather than sold as a separate add-on.
The AI-first framing is new. We will publish alumni stories only when the underlying outcome is verified and the person has consented to the use of their name or details.
No degree is assumed. Practical software experience is the important prerequisite: you should already be able to work in code and understand the basics of shipping a service.
No. It is designed for lateral-entry professionals who already work in or around software. The teaching starts with AI fluency, not syntax or a beginner programming sequence.
The realistic public estimate is 4–6 months part-time. Your exact schedule depends on your starting point, available weekly time, and the scope of the capstone.
Guidance-based pricing — talk to us. We discuss fit, background, and the delivery shape before quoting rather than placing a fixed shopping-cart price on a program that is meant to be adapted.