The Generative AI Career Masterplan : Navigate the Future of AI with Practical Insights from Industry Pioneers at AI-First Organizations.

Format:

Book

Author/Creator:

Arsanjani, Ali.

Language:

English

Subjects (All):

Arsanjani, Ali.

Hasan, Sadid.

Labonne, Maxime.

Horn, Andreas.

Kuligin, Leonid.

Artificial intelligence.

Career development.

Local Subjects:

Arsanjani, Ali.

Hasan, Sadid.

Labonne, Maxime.

Horn, Andreas.

Kuligin, Leonid.

Physical Description:

1 online resource (338 pages)

Edition:

1st ed.

Place of Publication:

Birmingham : Packt Publishing, Limited, 2026.

Summary:

Kick-start your agentic AI career with insights from leaders at Google, Microsoft, and Liquid AI.Learn the skills, strategies, and tools to build intelligent agents, transition faster, and lead in the era of autonomous systems.

Contents:

Cover

Title Page

Copyright and Credits

Dedication

Contributors

Table of Contents

Preface

Free benefits with your book

Part 1: The Gen AI Landscape: Understanding the Revolution

Chapter 1: Generative AI - Revolutionizing the Future of Work, Strategy, and the Global Economy

Understanding the advantages and risks of GenAI for jobs

Augmenting work and expanding possibilities with AI

The challenges and risks: Displacement, bias, and new threats

Rethinking corporate strategy and career paths

The agentic enterprise and the AI corporate citizen

Building corporate resilience amid uncertainty

Automating the entry level and reshaping the career ladder

How AI intersects with macroeconomic trends

The green transition

Geo-economic fragmentation and regulatory balkanization

A collective reshaping of the labor market

Labor's counteroffensive - unions negotiate for AI protections

Bridging the skills gap

The agentic enterprise - a refinement of GenAI

Defining the agentic enterprise

The shift to human-above-the-loop

The AI corporate citizen framework

New performance metrics (KPIs)

Organizational implications and new roles

The rise of the AI orchestrator

Efficiency gains (Klarna case study revisited)

Summary

Chapter 2: Decoding GenAI - Core Concepts for Every Professional

From discriminative to generative - a fundamental shift

The deep learning foundations of GenAI

In-context learning - how GenAI adapts on the fly

Prompt engineering - the art of asking AI the right way

AI-literacy - a new skill for the modern professional

What AI literacy means for professionals

The impact of AI literacy on careers

Chapter 3: Mapping Your Path - Roles and Roadmaps in GenAI

The shift from "models" to "systems".

From prototype momentum to product reality - understanding the gap

Why the demand is broad (and why this matters for career transitions)

Why agentic AI changes the career map

A practical map - five families of GenAI careers

How to use the map - a stable decision method

Core roles in GenAI - purpose, practice, and evidence

GenAI Engineer/AI Engineer

ML Scientist/Applied Scientist

AI/ML Researcher

AI Product Manager

AI UX/Conversation Designer

Data Scientist (AI focus)

Prompt Engineer/AI Developer

Ethical AI Advocate/Responsible AI Specialist

AI Governance/Risk Manager

AI Consultant/AI Strategist

Data Annotator/Human-in-the-loop (HITL) Specialist

Roles accelerated by agentic systems and production deployment

Choosing a career field in GenAI

Roadmaps and leveling matrices

Career pivot playbooks (by reader profile)

Part 2: The Gen AI Tech Stack: Architectures, Tools, and Advanced Concepts

Chapter 4: Generative AI Architectures and Advanced Concepts

Generative architectures

Transformer architectures

Mixtures of Experts

Hybrid architectures

Diffusion models

Prompt engineering

Evaluating prompts

Prompting techniques

RAG system design

Designing a RAG pipeline

Vector stores

Evaluating RAG

AI agents

Workflows vs. agents

Designing an agentic application

AI frameworks

Agentic harnesses

Chapter 5: Essential Tools and Platforms

Building cloud applications

Cloud model providers

OpenAI

Anthropic

Google

OpenRouter

Retrieval-augmented generation and agentic frameworks

LangGraph

LlamaIndex

Building local applications

Hugging Face Hub

Running models

GPU inference

CPU/GPU inference

Inference frontends

Fine-tuning models

Transformer reinforcement learning

Axolotl

Unsloth.

Experiment tracking

Evaluating models

Academic benchmarks

Custom evaluations

Part 3: Building Your AI Toolkit - Skills and Strategy

Chapter 6: Mastering Hard Skills - The Technical Edge

Foundational programming and data skills

Python as the language of AI

SQL as the language of data

Complementary languages and big data tools

Machine learning fundamentals for generative AI

Neural networks, deep learning, and the transformer revolution

Training, fine-tuning, and transfer learning

Language modeling - how LLMs generate text

ML and agentic frameworks - your workshop bench

Prompt engineering - the new technical literacy

Tools, frameworks, and techniques shaping GenAI work

Open-source ecosystems

Retrieval-augmented generation - grounding AI in facts

Evaluating generative AI outputs

Diverse technical backgrounds - interdisciplinary on-ramps to GenAI

From coding-only skills to human-AI collaboration

Chapter 7: Cultivating Technical and Human Skills for the Agentic AI Era

Foundational systems and execution concepts

The anatomy of agentic AI

Prompt optimization and structured reasoning

Fractal Chain-of-Thought (FCoT) and self-correction

DSPy and programmatic prompt compilation

Context engineering and epistemic memory

Pre-compaction memory flush

Hybrid search (Vector + SQLite FTS5)

Harness engineering: Governance and boundaries

Agent-to-Agent protocols and opaque execution

Containerized sandboxing and isolation

Strategic oversight: Human-Above-the-Loop

Redefining critical thinking: Instruction fidelity auditing

Non-Human Identity governance and ethical judgment

Privacy-preserving collaboration

Chapter 8: Strategic Career Moves: Entry, Transition, and Upskilling

Modern, emerging agentic AI roles.

Practical upskilling and transition pathways

The AI professional's job leveling matrix

Part 4: The Gen AI Landscape: Understanding the Revolution

Chapter 9: Applied Generative AI: Industry Use Cases and Multi-Agent Systems

Mastering strategic thinking about generative AI impact

How generative AI is transforming industries

Software development

Customer relationship management

Some engineering aspects of CRM applications

Marketing

Some engineering aspects of marketing applications

Finance

Some engineering aspects of financial applications

Healthcare

Some engineering aspects of healthcare applications

Multi-agentic systems

Further reading

Part 5: Landing Your Role and Sustaining Growth

Chapter 10: Building and Deploying LLM Applications

Application development

Evaluation

How to evaluate your application

Is developing generative AI solutions the same as application development?

From prototype to production

Reliability of your application

Detecting incidents

Resolving incidents

Integration and deployment

Permission management

Do you need a custom API for every agent?

Hosting an open-weight LLM yourselves

Chapter 11: Crafting Your AI Identity - Personal Branding for Impact

Why personal branding matters now

Why LinkedIn is the most underrated platform

Getting started with LinkedIn

Finding your mission

Writing your first LinkedIn post

How to write a post people will read

A formula to always find post ideas

Using AI to draft your posts the right way

How not to write your first LinkedIn post

Creating a repeatable content strategy

The hidden hack - maintaining a backlog

The hidden side of LinkedIn - networking and DMs

Using AI for personal branding

AI tools for LinkedIn.

Consistency - the real secret

Expanding beyond LinkedIn

Why you should start now

Chapter 12: Conquering the AI Job Market - Strategic Search Techniques and Interview Excellence

How AI is changing the job market

Laying the groundwork - defining your career objectives and identifying target companies

The four roles that actually exist in the AI job market

From random applications to a focused target list

Start building your resume

How to write an AI-resilient resume

Step 1: Get the format right (so you do not fail on a technicality)

Step 2: Write for search and for trust

Step 3: Keywords without keyword stuffing

Step 4: Make your resume AI-resilient by adding proof that systems cannot fake

Step 5: Keep LinkedIn and your resume consistent

Step 6: Use AI tools, but do not outsource your truth

Using AI tools for resume creation and ATS optimization

Using AI for cover letter writing

A resume is not enough - the portfolio as evidence

AI-assisted job search

Tips for job interview preparation

Step 1: Understand the company and role, or do not show up

Step 2: Build a story bank that you can deploy under pressure

Step 3: Prepare your deep dives, because that is where offers are decided

Step 4: Technical preparation, but targeted

Step 5: Prepare your questions, because they reveal your level

Step 6: Use AI for rehearsal, not for performance

Tools for interview preparation

Interview performance - the dos and don'ts that decide outcomes

Chapter 13: Expert Insights and Sustaining Growth - Building a Career That Compounds

Insider advice for the first 90 days

Days 1 to 30: Map reality and ship a quick win

Days 31 to 60: Run a small pilot with structured evaluation

Days 61 to 90: Harden, expand, and make outcomes visible.

Developing thought leadership and visibility.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

Other Format:

Print version: Arsanjani, Ali The Generative AI Career Masterplan

ISBN:

9781806691449

OCLC:

1610725782