Catalogue/Tier 2 · Job Roles/Engineering & Tech

AI+ AI/ML Engineers cover

T2-129 · Tier 2 · Job Roles

AI+ AI/ML Engineers

Build Better Models, Deploy Reliable Systems and Accelerate Your Full ML Lifecycle with AI

For the ML engineer whose experiment took two days when it should have taken one — and whose model card still isn't written.

Most ML engineers have adopted a code completion tool. Few have integrated AI systematically across the full ML lifecycle. Experiments still run long. Model cards still get skipped. This book gives you the FORGE Framework across five lifecycle stages, the LAUNCH deployment protocol, 50+ copy-ready prompt templates, and a 90-day plan to close the gap. Built by an AI engineering firm for engineers who build AI — and want to build it with the same rigour they expect from their own systems.

Tier

Tier 2 · Job Roles

Category

Engineering & Tech

Format

Guide

Updated

Q2 2026

Read full bookSee in 1 bundle

Inside

54 copy-ready prompt templates for code generation, debugging, evaluation, deployment, and documentation
The FORGE Framework — five lifecycle stages showing exactly where AI tools add most leverage in your ML work
The LAUNCH Protocol — six pre-deployment checks to run before every production release, completed in a sprint
The REFINE Diagnostic Loop for AI-assisted root cause analysis across classification, retrieval, and LLM failures
A 90-day accelerator plan calibrated to three engineering contexts: fintech, big tech, and regulated healthtech

Who this is for

Mid-career AI/ML Engineer (2–8 years' experience) at a tech company, AI start-up, fintech, healthtech, or enterprise AI team. Proficient in Python and at least one ML framework; experienced with model training, experiment management, and deployment. May already use GitHub Copilot for code completion but has not integrated AI tools systematically across the full ML lifecycle.

Also for:Data Scientists transitioning toward engineering roles; senior software engineers moving into ML; ML Tech Leads evaluating AI tooling adoption for their teams.

You’ll be able to

Apply the FORGE Framework to integrate AI tools systematically across the ML development lifecycle
Use AI code generation to build and refactor production-quality ML pipelines, training loops, and evaluation scripts
Diagnose model failures, bias, and drift using AI-assisted debugging and root-cause analysis techniques
Deploy and govern ML models using the LAUNCH Protocol — covering bias audits, interpretability documentation, monitoring, and stakeholder communication
Build LLM-powered applications and retrieval systems applying engineering rigour and responsible deployment practices

What’s inside

Diagnostic: How AI-augmented is your ML practice?
Chapter 1: AI in ML Engineering Right Now
Chapter 2: Faster Experiments with AI
Chapter 3: Code Generation for ML Systems
Chapter 4: AI-Assisted Feature Engineering and Data Work
Chapter 5: Model Development and Evaluation
Chapter 6: Debugging and Root Cause Analysis
Chapter 7: MLOps and Production Systems with AI
Chapter 8: LLM Engineering and Retrieval Systems
Chapter 9: Documentation, Model Cards and Stakeholder Communication
Chapter 10: The 90-Day AI ML Engineering Accelerator
Back matter: Skill Summary · Recommended Next Reads · Glossary · Tool Reference

Built by an AI engineering firm for engineers who build AI — and want to build it with the same rigour they expect from their own systems.

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