IT and Engineering: The Ultimate AI Goldmine

AI will create 170 million new jobs by 2030 while eliminating 92 million others. That’s a net gain of 78 million jobs, according to the World Economic Forum’s Future of Jobs Report. In an earlier newsletter on what AI means for your career, I break down which roles will explode, which skills will matter most, and where the 78 million new jobs will emerge.

There are a lot of headlines about job displacement, but the narrative is far more balanced than most headlines suggest. According to Silicon Valley Bank’s Innovation Economy Outlook report, layoffs in the tech sector have actually declined since ChatGPT’s launch in 2022. This challenges the narrative that GenAI is causing mass job displacement. The report attributes the late-2022 spike in layoffs to tighter funding, not AI automation.

Don’t get me wrong, AI is having an impact in different industries on that front, but AI will also create a wave of entirely new jobs, career paths, and business models. At the end of the day, AI needs humans to work.

Today, I want to break down exactly what’s happening in IT and Engineering, where the job creation is most explosive.

Five Areas Where AI is Creating the Most Jobs in IT and Engineering

If you’re in tech, you’re sitting on a goldmine right now. The engineering and IT sectors are weathering the AI storm, and they’re becoming the biggest beneficiaries.

Here’s what we’ll see emerge, especially in enterprise tech:

1. AI-native product development

Enterprise software is shifting from “AI-enhanced” to AI-native platforms. This shift will create entirely new job categories:

• AI product managers (AI PMs) manage end-to-end development of enterprise AI tools. They need deep understanding of ML model lifecycles and enterprise pain points. Think traditional PM skills plus AI fluency.
• AI UX designers craft intuitive experiences for complex AI systems. They specialize in data labeling interfaces, explainability dashboards, and building user trust in AI decisions.
• Prompt engineers and workflow designers are key for enterprise copilots. They build task-specific interactions within platforms like Salesforce, SAP, and ServiceNow.

These roles exist because tools like Microsoft Copilot, Salesforce Einstein, Google Duet AI, and Notion AI need specialists who understand both the technology and business context.

2. AI-driven cloud infrastructure and DevOps (AIOps)

The rise of AIOps (AI for IT Operations) is transforming how companies manage their backend systems.

• MLOps engineers manage model deployment, monitoring, and versioning across enterprise environments. Every company needs these skills to move from AI experiments to production.
• AIOps specialists configure AI for performance tuning, anomaly detection, and auto-remediation in large-scale infrastructure. They’re the people who make sure your AI systems work reliably.
• Cloud-native AI architects design scalable AI workflows using AWS, Azure, or GCP with cost and performance efficiency in mind.

Related roles will pop up, including: Observability Engineers, Incident Prediction Analysts, and Infrastructure Prompt Scripters.

3. Cybersecurity and AI trust

AI expands the attack surface but also enables faster threat detection. Enterprise security is one of the fastest-growing job categories right now.

• AI cyber analysts use machine learning to detect and respond to real-time security incidents, especially in Security Operations Centers (SOCs).
• Red teamers for LLMs simulate prompt injection, jailbreaks, and hallucinations in enterprise AI apps. Yes, people are literally paid to try to break AI systems.
• AI risk and compliance officers ensure enterprise deployments adhere to evolving AI governance frameworks like the EU AI Act and NIST standards.

4. Enterprise data and knowledge infrastructure

AI thrives on data, which means massive demand for data-centric roles:

• Data engineers and stewards clean, tag, and prep enterprise data for LLM consumption. AI is only as good as its training data, and this work remains fundamentally human.
• Vector database engineers manage RAG (retrieval-augmented generation) pipelines using tools like Pinecone, Weaviate, or Chroma. These are the people who help AI systems access the right information at the right time.
• AI knowledge graph designers structure enterprise knowledge for real-time AI access and synthesis.

5. Vertical SaaS AI specializations

Industry-specific AI deployments are spawning hybrid roles that require both domain knowledge and AI fluency:

• LegalTech AI specialists work on platforms like Ironclad or Lexion, focusing on contract AI and legal document analysis.
• FinTech AI analysts handle fraud detection, KYC/AML compliance, and personalized finance workflows.
• AI in HR tech specialists focus on sourcing and evaluating candidates using AI tools like SeekOut or Paradox.

$632 Billion in AI Spending by 2028

According to IDC, worldwide AI spending on AI (including applications, infrastructure, and IT services) will reach $632 billion in 2028. This will lead to new AI roles in engineering, product, data, and operations to maintain these AI systems.

Enterprise vendors like Microsoft, Google Cloud, and Oracle are investing billions in AI-driven platforms. This isn’t a bubble — it’s sustained job growth for at least the next decade.

What Does This Mean for Your Career

If you’re already in tech, you have a massive advantage. The foundation is there — you just need to add AI skills on top. But don’t wait. The window for easy career transitions is closing fast.

Here’s my advice:

If you’re a software engineer: Start with Cursor or GitHub Copilot for code reviews, then learn prompt engineering fundamentals. Pick up basic MLOps by deploying a simple model to AWS SageMaker or Azure ML. Focus on becoming an AI-assisted developer first — companies are hiring more engineers because AI makes teams 2x more productive.

If you’re in DevOps/Infrastructure: Dive into AIOps tools like Datadog AI or New Relic’s ML capabilities. Learn vector databases. Get hands-on with Kubernetes for ML workloads. The path from traditional DevOps to MLOps Engineer is shorter than you think.

If you’re a product manager: Study AI product management frameworks and experiment with enterprise AI tools in your current role. Take a course on ML basics (you don’t need to code, but you need to understand model limitations). Shadow engineering teams during AI feature releases. AI PMs will command 20-30% salary premiums.

If you’re in cybersecurity: Learn how to red team LLMs using tools like IBM’s PromptBreach or custom scripts. Study AI governance frameworks (NIST AI RMF, EU AI Act). Practice detecting AI-generated phishing attempts. Security + AI knowledge will be one of the highest-demand combinations.

If you’re a data engineer: Focus on RAG pipelines and vector databases. Learn tools like LangChain, Chroma, or Weaviate. Practice data preparation for LLM training. Data engineers who understand AI workflows are invaluable.

If you’re a UX designer: Study AI interface patterns from tools like Notion AI, Figma AI, or Microsoft Copilot. Learn about explainable AI and trust-building UX patterns. Practice designing for AI uncertainty and error states. AI UX designers will be highly valued.

Bottom line: The engineers who adapt fastest will have the most opportunities.

And if you’re not in tech yet? Now might be the time to make the switch. The demand for AI-adjacent roles is creating more entry points than we’ve seen in years.

What’s Coming Next

This is just the beginning. In my next posts, I’ll discuss the AI job opportunities emerging in healthcare, finance, education, and other sectors. The AI job market is going to explode. The question isn’t whether these opportunities exist — it’s whether you’ll position yourself to take advantage of them.