Top Emerging Career Paths for Job Seekers

If you're in tech, you're sitting on a goldmine right now. While everyone's debating AI job displacement, the engineering sector is quietly becoming the biggest AI beneficiary. The World Economic Forum projects 78 million net new jobs by 2030, and IT and Engineering is leading the charge. This shift is creating entirely new job categories that didn't exist two years ago. Here are five emerging growth areas for IT and Engineering: 1. AI-native product development → AI Product Managers who understand ML lifecycles and enterprise pain points. 2. AIOps infrastructure → MLOps engineers are moving companies from AI experiments to production. Every enterprise needs these skills. 3. AI cybersecurity → Red teamers for LLMs are literally paid to break AI systems.  4. Enterprise data infrastructure → Vector database engineers managing RAG pipelines are helping AI systems access the right information at the right time. 5. Vertical AI specializations → LegalTech AI specialists, FinTech AI analysts, HR tech AI specialists—domain expertise + AI fluency is the new superpower. The numbers back this up: $632 billion in AI spending (including applications, infrastructure, and IT services) by 2028. This will lead to new AI roles in engineering, product, data, and operations to maintain these AI systems. Bottom line: The engineers who adapt fastest will have the most opportunities. In my latest newsletter, I break down exactly how to transition into each of these roles, plus the specific tools and skills that matter most. What AI role are you most curious about? #AI #Engineering #IT #FutureOfWork

⚠️ 170 million new jobs will be created by 2030. (And 92 million will be lost.) → That’s a 22% churn in the global job market According to the latest WEF report. But these new jobs? They won’t look like the ones we know. The AI era isn’t just about automation It’s about reinvention. Here are 20+ AI-powered roles already reshaping the workforce: → Prompt Engineer – designs the inputs that guide AI behavior → AI Risk & Governance Specialist – ensures systems are safe, fair, and aligned → Decision Engineer – builds shared workflows between humans + machines → AI Ethicist – defines the moral boundaries of what AI should do → ML(Ops) Engineer – deploys and monitors machine learning systems → Head of AI – sets the AI vision and strategy for an entire org → Data/AI Translator – bridges business needs with AI solutions → Model Validator – stress-tests AI models for accuracy and bias Some of these barely existed 3 years ago. Now they’re must-haves. 🔁 We’re moving from data → to decisions → to agents. AI isn’t just predicting outcomes. It’s taking action. And that shift? It’s transforming who we hire, how we work, and what we need to learn. The real edge? Not just knowing AI. But knowing how to build it, guide it, and govern itt ogether. Want to future-proof your career? Start here: Learn the language – Understand LLMs, agents, and AI systems Pick a lane – Technical (ML, data, tools) or Strategic (governance, ethics, ops) Build something – Start with ChatGPT, LangChain, HuggingFace, etc. Think beyond tools – Learn decision-making, ethics, and design thinking Stay curious – The fastest learners will lead this wave 👇 This chart is your career cheat sheet. 🔁 Repost to help your network stay ahead ✅ Follow Gabriel Millien for future-ready insights on AI, careers, and transformation

We talk a lot about the jobs AI might replace. But not enough about the ones it’s creating.  WEF’s latest Future-of-Jobs pulse shows a 22 % global role-swap by 2030: 🔹 170 M brand-new roles created 🔹 92 M roles sunset That net gain is powered almost entirely by AI. Fastest-growing titles right now • AI & ML Specialist • Big-Data Analyst • Fintech Engineer • Data-Lake Architect • AI-augmented UX Designer • Info-Sec Analyst (LLM threat model focus) • Process-Automation Lead Most of these job names were niche—or nonexistent—five years ago. And it’s not just code The new org charts fill with hybrids: • AI Ethics Lead • Prompt Engineer • AI Product Strategist • Decision Engineer (designs human+machine workflows) • Risk & Governance Specialist for AI These sit at the crossroads of AI × Business, AI × Design, AI × Policy. What this means for companies Hiring “a data-science team” is table stakes. Interdisciplinary fluency is the moat. Leaders must learn to think with AI—strategically, creatively, responsibly. I’m helping execs build exactly that mindset: how to pair domain expertise with generative intelligence and stay ahead of the churn. Want the full WEF breakdown + Gartner role map? Drop “AI Talent Map” below and I’ll DM the PDF. ♻️ Repost to put opportunity—not fear—at the centre of the AI jobs debate. Follow Gaurav Bhattacharya for daily GTM + AI insights. #FutureOfWork #AIJobs