Is this the same as the AI Engineer bootcamp?

No. The bootcamp is a longer cohort programme. This pathway is a four-week private engagement for one learner, requires technical entry experience, and moves at a full-time accelerated pace.

Is everything live teaching?

No. Your time combines private instruction, guided laboratories, independent engineering work, code reviews, and assessment, and you are expected to complete structured self-study alongside the mentored day.

Is Career+ really included?

Yes. Career+ career support is included in this private pathway at no extra cost, where it is often an optional paid add-on elsewhere. It covers CV, LinkedIn, interview preparation, and job search support after you finish, delivered one-to-one. It does not guarantee a job offer.

Can the curriculum be personalised?

Yes. The four-week architecture remains intact, but your mentor adjusts examples, technical depth, tooling, and capstone decisions to your diagnostic and professional goal.

How do deposit and instalments work?

There is no instant checkout. After acceptance, an advisor confirms your start date and agrees a deposit and instalment schedule with you before enrolment.

Does this guarantee a job?

No. The pathway builds assessed production capability and professional evidence, but employment and salary outcomes depend on your experience, market, performance, and job search.

What happens after I apply?

We review your experience and availability, arrange a private consultation and technical diagnostic, confirm fit, then reserve a start date if you are accepted.

Home PathwaysAI Engineer Accelerated

Private 1:1 pathwayOne learner per intakeCareer+ included

Four-week full-time engagement

AI Engineer Accelerated Pathway

Four weeks of private AI engineering, shaped around your starting point and goal. Build from production ML foundations to LLM systems, RAG, agents, MLOps, and a deployed capstone, then continue into Career+ support.

Guided plus self-studyDedicated senior mentorProduction capstone

Explore the full curriculum

Founding client rate

$10,999$11,999

Payment plan agreed privately after acceptance. No instant checkout.

Application only

1 learner per intake

4 weeks

Full-time intensive

1:1

Private delivery

1 learner

Per intake

Career+

Included

Your four stages

One capability unlocked each week

The pathway moves through four engineering stages. Each stage ends with an assessed sign-off before the next begins.

Week 1 · Model
ML and Deep Learning Foundations
Train, evaluate, and explain reliable models with production discipline.
Week 2 · Language
NLP, Transformers, and LLM Apps
Ship structured, tool-enabled LLM applications with tests and guardrails.
Week 3 · Ground
RAG, Vector Systems, and Agents
Build cited, evidence-led systems and bounded agents you can trust.
Week 4 · Ship
MLOps, Deployment, and Capstone
Deploy, monitor, and defend a production AI system end to end.

Full curriculum

Four weeks. One capability unlocked at a time.

Every day has a technical focus, guided lab, assessed deliverable, and mentor review. Expand each week to inspect the complete plan.

Python engineering workspace showing code, tests, and a data pipeline — Week 1 engineering environment

Day 1

Engineering baseline and diagnostic

Establish your private learning plan and production environment.

Topics

Python and data workflow diagnostic
Git branching and repository standards
AI system lifecycle
Reproducible environments

Guided lab: Configure a professional AI repository, environment, quality checks, and experiment notebook.

Assessed deliverable: Personal capability map and production-ready project scaffold.

PythonGitJupyterVS Code

Day 2

Data preparation for modelling

Turn raw data into a defensible training dataset.

Topics

Data profiling
Missing data and outliers
Feature engineering
Leakage prevention

Guided lab: Build a validated preprocessing pipeline with train, validation, and test splits.

Assessed deliverable: Reusable data pipeline with quality report.

PandasNumPyscikit-learn

Day 3

Supervised learning systems

Select and compare appropriate predictive models.

Topics

Regression and classification
Baseline selection
Cross-validation
Hyperparameter search

Guided lab: Train and compare multiple models against an explicit baseline.

Assessed deliverable: Model comparison report and selected candidate.

scikit-learnXGBoostMatplotlib

Day 4

Evaluation, fairness, and explainability

Prove what the model can and cannot do.

Topics

Task-specific metrics
Error analysis
Calibration
Bias and explainability

Guided lab: Create an evaluation harness and explain predictions for key user segments.

Assessed deliverable: Evaluation scorecard with risks and mitigations.

SHAPscikit-learnSeaborn

Day 5

Neural networks and week-one review

Move from classical ML to practical deep learning.

Topics

Tensors and computational graphs
Training loops
Regularisation
Experiment tracking

Guided lab: Train a neural network and compare it with the classical baseline.

Assessed deliverable: Week-one technical review and signed-off modelling repository.

PyTorchMLflowTensorBoard

Week 1 sign-off: A reviewed ML repository with a baseline, experiment log, evaluation report, and deployment-ready model artefact.

Your working day

A full-time rhythm built for mastery

Each weekday balances direct teaching, guided practice, focused build time, and review. Alongside the mentored day, you are expected to put in structured self-study to consolidate what you build.

09:00Private instruction and architecture

12:00Guided laboratory

14:00Independent engineering sprint

16:30Code review, evaluation, and planning

How your time is spent

The activity mix, visualised

Private instruction

35%

Concept teaching, architecture sessions, and direct technical coaching.

Guided engineering labs

25%

Mentor-defined builds with immediate feedback and correction.

Independent build work

20%

Focused implementation with targeted mentor support when needed.

Self-study and review

20%

Structured reading, practice, code reviews, and capstone preparation in your own time.

What you will be able to do

Production capability, not surface-level familiarity

Train and evaluate classical and deep-learning models using reproducible engineering practices

Build structured, tool-enabled LLM applications with evaluation and safety controls

Design cited RAG systems with ingestion, hybrid retrieval, reranking, and abstention

Create bounded AI agents with permissions, approval gates, and recovery paths

Deploy containerised AI services with CI/CD, monitoring, runbooks, and cost controls

Present a production capstone through a private architecture and technical defence

Technical toolkit

Tools used across the pathway

PythonPyTorchscikit-learnHugging FaceVercel AI SDKNext.jsTypeScriptLangChainLlamaIndexpgvectorFastAPIDockerGitHub ActionsTerraformMLflowOpenTelemetry

Tools follow the engineering decision

Your mentor may substitute equivalent production tools where your capstone, employer stack, or target role requires it.

AI engineer presenting a production capstone to a senior mentor

Your capstone defence

Leave with evidence you can explain

Present the working system, defend your architecture, demonstrate evaluation evidence, and show how you would operate it in production.

Working AI productArchitecture recordEvaluation evidenceTechnical defence

Included with your private pathway

Career+ comes built in

Career+ is included in this pathway at no extra cost, and delivered one-to-one by the same mentor who has just watched you build and defend a production AI system. Elsewhere it is usually an optional paid add-on shared across a group.

Because the work is private, your career support is built around you specifically: your capstone, your background, and the exact roles you are targeting. We cannot promise an offer, but no part of your positioning is left generic.

What Career+ covers

A private positioning session where your mentor turns your capstone into evidence employers trust
CV and LinkedIn rewritten one-to-one around your assessed AI engineering work
Personalised mock interviews using questions matched to your target roles
A focused job search plan built with your mentor, with follow-up check-ins after you finish

Being honest: Career+ improves your readiness and evidence. Employment and salary outcomes still depend on your experience, market, and performance.

Career context

Roles this capability supports

Illustrative market ranges only. Compensation varies by location, experience, employer, and evidence of production capability. This programme does not guarantee employment or salary outcomes.

RoleUKUS

AI Engineer£55k - £95k+$120k - $180k+

LLM Application Engineer£60k - £100k+$130k - $190k+

MLOps Engineer£60k - £105k+$125k - $185k+

Machine Learning Engineer£55k - £95k+$115k - $175k+

Certificate and professional evidence

Successful completion requires delivery of the assessed work and a satisfactory capstone defence. You receive an Xcademia certificate, private assessment summary, architecture documentation, evaluation evidence, and a portfolio-ready repository.

Entry prerequisites

You need working Python, confidence with Git and command-line workflows, and enough technical experience to learn full-time. This pathway is not suitable for complete programming beginners. Every applicant completes a diagnostic before acceptance.

Private programme investment

A senior mentor focused on one learner