How to Prepare Students for AI Careers

If you’re in your university or aiming for a career in AI/ML - this is for you. (Also addressing some great feedback I received from my last post!) A few months ago, I had a short call with a university student trying to break into AI. We sketched out a realistic roadmap - not perfect, but focused. I’m sharing that updated version here, along with a few key lessons from the thoughtful feedback I got: Let’s be honest: University often gives you lots of theory (math, stats, proofs which are super important!). But what it doesn’t always give you is production-level practical experience. Here’s a roadmap to help balance both. 📘 If I had to start again today, I’d focus on just 2-3 things first: → Python + SQL + PyTorch → Cloud Basics (AWS or GCP) → Model Deployment (CI/CD + MLOps basics) But if you want the extended version - here’s a full roadmap, with some additions based on community feedback: 1️⃣ 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀 → Python (non-negotiable) → Learn to write real code, not just notebooks → Get familiar with APIs (FastAPI / Flask) 2️⃣ 𝗖𝗹𝗼𝘂𝗱 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀 & 𝗠𝗟𝗢𝗽𝘀 → Start with AWS - ML certification or AI Practitioner are structured and beginner-friendly → Learn basics of CI/CD (GitHub Actions, etc.) → Try MLflow or SageMaker for simple experiments → Learn Docker and basic containerization → Terraform if you’re infra-curious 3️⃣ 𝗠𝗼𝗱𝗲𝗹 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 → PyTorch → XGBoost for tabular use-cases → Hugging Face basics for LLMs 4️⃣ 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 (𝗬𝗘𝗦, 𝘁𝗵𝗲𝗼𝗿𝘆 𝗺𝗮𝘁𝘁𝗲𝗿𝘀!) → Understand core ML Algorithms and concepts: overfitting, regularization, loss functions → Get comfortable with mathematics, stats and linear algebra → Learn how to think about models, not just how to run them You don’t need all the math proofs, but intuition is key - especially if you’re aiming for long-term depth. 5️⃣ 𝗠𝗼𝗱𝗲𝗿𝗻 𝗔𝗜 𝗦𝘁𝗮𝗰𝗸 𝗔𝘄𝗮𝗿𝗲𝗻𝗲𝘀𝘀 → LLMs, RAG, Agents - awareness is enough at first → Focus more on how the tools solve problems than chasing every new framework 6️⃣ 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 → Pandas (always), Polars or DuckDB (bonus) → Spark for large-scale workflows 7️⃣ 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 + 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 → Learn how to evaluate models in production (A/B testing, feedback loops) → Understand logging, monitoring, and debugging models post-deployment A final reminder: You don’t need to master every tool. What matters most is problem-solving mindset and adaptability - tools will change, fundamentals won’t. And yes - if vibe coding, asking “why” before “how,” and connecting technical work to business outcomes will take you further than any tool ever can. What would you add to this? 👇 Share your thoughts in the comments! - ♻️ Repost if you found it helpful 🤗 ➕ Follow me - Shantanu for Production AI - ML - MLOps content and Career tips!

What Should an AI Literacy Course for All Business Students Look Like? I have been developing a hypothetical course outline as a way of exploring what every business student, regardless of their major, may need to thrive in an AI-intensive economy. My goal is to understand what foundational AI literacy should look like across an undergraduate business degree. I am sharing the outline in the attached document to prompt discussion and feedback. The design brings together several strands that employers are increasingly expecting: 1. Understanding how generative and agentic AI systems actually work. Students need a realistic grasp of how AI produces outputs, why it fails, how settings influence behaviour, and how emerging agentic systems can plan and use tools. This gives students across all business disciplines a more accurate mental model of AI. 2. Using structured prompts, JSON, and schemas to achieve reliable, auditable outputs. Most students (and many professionals) learn prompting informally. Structured prompting, combined with JSON and schema design, helps students create clearer, more reproducible, and more professional outputs. This will also make them better students by improving the quality of their AI-supported academic work. 3. Building and supervising simple AI workflows. Students gain experience with safe, small-scale agentic workflows. They learn how to structure multi-step tasks, apply validation, and maintain human-in-the-loop oversight. These skills are essential in various fields, including accounting, marketing, analytics, operations, entrepreneurship, and more. 4. Developing ethical awareness and cultural competence. Because the course uses structured, auditable AI processes, students learn to identify hallucinations, bias, privacy risks, and cultural missteps. Integrating Māori data sovereignty and New Zealand governance frameworks encourages responsible, locally grounded practice. This foundation would enable students to become more capable AI users, stronger critical thinkers, and more effective learners in all their other courses. It also sets them up for deeper dives in analytics, IS, marketing, operations, innovation, and entrepreneurship. I am sharing the outline (attached) as a prototype of such a course to ask two questions of colleagues in business education and industry: 1. Is your business school teaching anything similar? If not, why not? 2. How could this design be improved to better serve future graduates? I would welcome insights, critiques, extensions, and examples of courses that are already moving in this direction. Thanks in advance for your insights! #AILiteracy #AIinBusiness #BusinessSchools #FutureOfWork #ResponsibleAI #DataLiteracy #HigherEdInnovation

Everyone wants to “work in AI” — but what does that actually mean? Lately, I’ve met so many students saying “I want to build a career in AI”. But when I ask which part of AI excites you?, there’s silence. Here’s the thing — AI isn’t one job. It’s a vast ecosystem. If you’re a high school student dreaming of AI, let’s unpack this for you: 🧠 AI Branches You Could Work In: • Machine Learning: Algorithms that improve over time (think Netflix recommendations) • Natural Language Processing (NLP): Teaching machines to understand human language (like ChatGPT) • Computer Vision: Enabling computers to ‘see’ images and videos (used in autonomous cars) • Robotics: AI-driven physical machines (like Boston Dynamics’ robots) • Generative AI: AI that creates text, art, music, videos, code 🎓 Relevant Degrees & Pathways: • BTech/BE in Computer Science, Data Science, or AI & ML • BSc in Mathematics, Statistics, or Data Analytics • BDes (for AI+Design applications like UI/UX for AI products) • BA in Cognitive Science, Linguistics (for NLP) • BSc in Neuroscience, Psychology (for AI in Healthcare/Brain-Machine interfaces) 💡 If I were in high school today and wanted to work in AI, I’d: 1. Start with Python basics on platforms like Coursera or Kaggle 2. Build projects like a chatbot, a face-recognition app, or a text summarizer 3. Read AI ethics papers (because with great power comes great responsibility) 4. Participate in AI/ML hackathons or competitions 5. Learn how AI connects with other fields: design, psychology, finance, healthcare AI isn’t one lane — it’s a superhighway with multiple exits. Find the one that excites you. If you’re a student exploring AI, drop a comment and let’s chat about which path might be right for you. I’m happy to help. #artificialintelligence #ai #futureofwork #careerguidance #growthmindset Disclaimer: This is not an exhaustive list — AI is a rapidly evolving field with diverse, interdisciplinary pathways. Think of this as a starting point to explore your options.

The post-12th journey no longer starts with asking, “𝐒𝐜𝐢𝐞𝐧𝐜𝐞, 𝐂𝐨𝐦𝐦𝐞𝐫𝐜𝐞, 𝐨𝐫 𝐀𝐫𝐭𝐬?” 𝐛𝐮𝐭 𝐫𝐚𝐭𝐡𝐞𝐫, “𝐖𝐡𝐢𝐜𝐡 𝐟𝐮𝐭𝐮𝐫𝐞 𝐚𝐫𝐞 𝐲𝐨𝐮 𝐩𝐫𝐞𝐩𝐚𝐫𝐢𝐧𝐠 𝐟𝐨𝐫?” In the AI-driven world, choosing a career is not about picking a degree — it’s about building a portfolio of skills, tools, and adaptability that can survive rapid disruption. With tools like 𝐂𝐡𝐚𝐭𝐆𝐏𝐓, 𝐁𝐚𝐫𝐝, 𝐌𝐢𝐝𝐣𝐨𝐮𝐫𝐧𝐞𝐲, 𝐍𝐨𝐭𝐢𝐨𝐧, 𝐅𝐢𝐠𝐦𝐚, 𝐚𝐧𝐝 𝐆𝐢𝐭𝐇𝐮𝐛 𝐂𝐨𝐩𝐢𝐥𝐨𝐭 becoming embedded into daily workflows, the very definition of "work readiness" has changed. Today, 𝐩𝐫𝐨𝐦𝐩𝐭 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠, 𝐝𝐚𝐭𝐚 𝐥𝐢𝐭𝐞𝐫𝐚𝐜𝐲, 𝐧𝐨-𝐜𝐨𝐝𝐞 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧, 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧, 𝐚𝐧𝐝 𝐀𝐈-𝐚𝐬𝐬𝐢𝐬𝐭𝐞𝐝 𝐝𝐞𝐬𝐢𝐠𝐧 𝐚𝐫𝐞 𝐛𝐞𝐢𝐧𝐠 𝐥𝐢𝐬𝐭𝐞𝐝 𝐚𝐬 𝐝𝐞𝐬𝐢𝐫𝐚𝐛𝐥𝐞 𝐬𝐤𝐢𝐥𝐥𝐬 𝐢𝐧 𝐣𝐨𝐛 𝐝𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧𝐬 𝐛𝐲 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐚𝐜𝐫𝐨𝐬𝐬 𝐬𝐞𝐜𝐭𝐨𝐫𝐬 — from media and finance to healthcare and manufacturing. 🎯 𝐒𝐭𝐮𝐝𝐞𝐧𝐭𝐬 𝐚𝐢𝐦𝐢𝐧𝐠 𝐭𝐨 𝐚𝐥𝐢𝐠𝐧 𝐰𝐢𝐭𝐡 𝐀𝐈-𝐫𝐞𝐥𝐚𝐭𝐞𝐝 𝐜𝐚𝐫𝐞𝐞𝐫𝐬 𝐜𝐚𝐧 𝐩𝐮𝐫𝐬𝐮𝐞: ✅ B. Tech or BSc in Computer Science / AI / Data Science ✅ BBA in Business Analytics / Digital Business / Fintech ✅ BA in Cognitive Science / Philosophy with AI ethics focus ✅ B. Com with electives in Quantitative Techniques, Business Intelligence ✅ B.Des with UX/UI specialization integrated with AI tools The sooner students move from consumption to creation, the better. 🎯 Even after class 12, they can: ✅ Contribute to open-source AI projects ✅ Start a blog or Substack sharing AI tool reviews or learning journeys ✅ Build a chatbot using ChatGPT or Bard integrations ✅ Apply for virtual internships via platforms like Internshala, AICTE NEAT, and Turing ✅ Attend AI summits, youth innovation bootcamps, and community hackathons By integrating AI, even traditional careers now come with a tech twist. Emerging and hybrid roles include: ✅AI Business Analyst ✅Machine Learning Engineer ✅AI Ethicist / AI Policy Advisor ✅UX Designer with Conversational AI focus ✅Fintech Product Manager ✅Cybersecurity Analyst (AI-powered risk prediction) ✅AI-Assisted Content Strategist ✅Digital Transformation Consultant Hiring trends reported by LinkedIn, Naukri. com, and McKinsey & Company clearly indicate a shift toward skill-first hiring. Roles like AI operations manager, digital ethicist, cybersecurity strategist, product content analyst, and sustainability analyst are emerging — roles that didn’t even exist in a typical career counselling session five years ago. Because the future isn’t waiting for your child to finish school. It’s already recruiting, automating, adapting — and rewarding those who start early. #aitools #cybersecurity #aiengineer #artificialintelligence #machinelearning #robotics #careerprospect #careerdevelopment #skillsdevelopment

Schools need to focus on AI life skills in teaching and learning. Teaching artificial intelligence in education largely centers around making sure students and teachers know about AI—what it is, how it works, which tools to use, and how to fact-check responses. These AI-literacy skills are important, but if we only teach about AI we miss a critical opportunity to practice enhancing our human abilities with AI. In addition to just knowing about AI, students need to practice using AI to think deeper, create better, and solve problems more efficiently than they could on their own. Many schools have created portraits of a graduate - frameworks that articulate the durable skills students should have by the time they graduate (beyond the subject-area knowledge about math, science, history, etc). Adopting that approach, I’ve created a "Profile of an AI-Ready Graduate,” which was shared at the recent #ISTELive and #ASCDAnnual conference. It identifies six core roles students should be comfortable taking on–with AI–to maximize their human potential. ✴️ Learner Students know how to use AI to set learning goals, create plans for learning new skills, identify strategies to get unstuck, and seek targeted feedback to improve performance and understanding. ✴️ Researcher Students know how to use AI to investigate and analyze topics, evaluate claims, and compare sources of information. ✴️ Synthesizer Students know how to use AI to synthesize, remix, and refine information into formats and levels of complexity that best meet their unique needs and capabilities. ✴️ Ideator Students know how to use AI as a brainstorming partner to generate new ideas and explore a wide range of possibilities. ✴️ Connector Students know how to use AI to increase human collaboration, including overcoming language barriers, and finding common ground among divergent perspectives. ✴️ Storyteller Students know how to use AI to present and communicate complex ideas through text, image, audio, video, and other media. The Profile of an AI-Ready Graduate provides a roadmap for helping students learn to use AI to enhance and build on their uniquely human capabilities. By modeling and teaching the key roles students will be expected to take on, we can better prepare them for a world in which AI will be increasingly integrated into their lives. There is no question that students need to learn about AI. But to thrive (and survive) in a AI-powered world, they also need to know how to work with AI creatively, thoughtfully, and strategically. We must shift the conversation from one of basic theoretical understanding to one of in-depth practical and creative applicability. Anything less would be limiting their future success. ISTE ASCD Anthony Rebora Joseph South

My Suggestion to Young Computer Science Undergrad Students in Nepal When these young students graduate in a year or two, there will be very few jobs available to them. This is because the entry-level jobs traditionally given to fresh software engineers will increasingly be done by AI. This change is happening globally, and Nepal won’t be an exception. So, young students must prepare themselves to be productive at the level of a senior engineer — with the help of AI. If you rely only on what you’re learning in college and expect a company to train you after hiring, you will be left behind. Here are stepwise suggestions tailored for the Nepali context: 1. Learn to Communicate with GenAI Tools Like ChatGPT, Gemini, and MetaAI You should know how to ask the right questions, and then refine those questions again and again until the tool gives you something meaningful. Most students in Nepal still don’t know how to use these tools well — mastering them early will give you a serious edge. 2. Use AI to Prepare Real Work Artifacts Start using AI to write technical documentation, QA plans, and code. Even if the output is not always perfect, you will learn by reviewing and fixing it. Have the patience to debug AI-generated code and edit AI-written documents. That skill will be more important than memorizing syntax. 3. Treat AI as Your Personal Tutor In Nepal, not everyone has access to high-quality teachers or mentors. That’s where AI can fill the gap. You have a 24/7 assistant that can help you understand concepts, debug code, and explain things clearly. Learn how to use it well. 4. Present Yourself as “AI-Powered” When Applying for Jobs When you graduate, you should be able to show that you can work like a team of three people — because you know how to use AI. Employers in Nepal, especially in startups or outsourcing companies, want efficiency. They will hire someone who can do more with less — and AI makes that possible. 5. Build a Portfolio that Reflects AI-Augmented Development Don’t just say you can use AI — show it. Build projects (websites, automation tools, apps) using AI tools, and publish them on GitHub or your own portfolio. Make it clear that you know how to use AI to speed up development and produce quality output. Final Note for Nepali Students: AI is not here to replace you — it’s here to work with you. If you stay passive, you will fall behind. But if you embrace AI early and smartly, you can leapfrog many of your peers — not just in Nepal, but globally.

The AI revolution isn't what you think. Forget the hype about replacing jobs. It's creating entirely new careers. Here's what's emerging (and how to prepare): 1. Development Teams ↳ Prompt Engineers • Master prompt crafting • Learn LLM capabilities • Study system design ↳ AI Model Validators • Deep dive into testing frameworks • Learn bias detection • Study performance metrics ↳ Decision Engineers • Focus on algorithmic thinking • Learn decision theory • Master data visualization 2. Risk & Governance ↳ AI Ethicists • Study tech ethics • Learn bias mitigation • Understand regulatory frameworks ↳ Compliance Specialists • Master AI regulations • Learn risk assessment • Study industry standards 3. Business Integration ↳ AI Product Managers • Learn AI capabilities • Master stakeholder management • Understand use case design ↳ Business Translators • Develop technical literacy • Master communication • Learn change management Want to upskill? Start here: • Take online courses - AI For Everyone – Andrew Ng - Machine Learning Specialization – Coursera - Practical Deep Learning – fast.ai - CS50 AI – Harvard edX - LLM Certificate – Databricks - Elements of AI – Helsinki • Join AI communities • Build practical projects • Follow industry leaders • Attend workshops The truth is: AI success isn't just about tech. It's about building the right expertise. The next 24 months will be crucial. Start preparing now. P.S. Which role interests you most? Drop a comment with your learning journey. Recommend the best courses and resources to fellow readers. — ➕ Follow me for more insights on business evolution, ♻️ Repost to educate your LinkedIn network!

College isn’t the differentiator. This is. One person + AI can now outperform a whole team. Are you ready? A diploma still opens doors, but today it does not show who will perform at the highest level. Hard skills from class? Most graduates leave with the same ones. The true edge today is this: → Mastering AI. A lawyer must now be a tech lawyer. A doctor, a tech MD. A biologist, a tech biologist. Every field is shifting. I have seen it again and again-one person with the right AI tools can do what used to take five or ten. Ignore this, and the gap only grows. Here’s what I tell every parent and every leader: 1/ Look for AI-first spaces Schools, projects, companies. Everything built around AI, not just using it as a side feature. 2/ Go “AI-native” Jump into learning where AI is central. Pick tools and projects where you use AI in every step. 3/ Build your AI network Grow your circle-find friends, mentors, and teams who think and build with AI in mind. 4/ Get hands-on Side projects, business cases, real world problems. Try, fail, learn, repeat. Don’t wait for “perfect.” 5/ Grow your human skills Collaboration, influence, empathy. These are the glue in the AI era (not less, but more important now). This isn’t just for students. If you lead a team: Are you helping your people become AI-native? If you’re a parent: Are you preparing your kids for a world where AI is at the core? The future is arriving faster than we think. How are you making this real-in your family, your company, your own learning? #LinkedInTopColleges #AI #Leadership #FutureOfWork

LinkedIn’s Chief Economic Opportunity Officer, Aneesh Raman, recently wrote an op-ed on how AI is breaking entry-level jobs, the very roles freshers rely on to launch their careers. I couldn’t agree more. Even if you’ve only used AI tools like ChatGPT, Claude, or Gemini casually, you’ve likely noticed how easily they handle tasks like writing reports, analyzing data, creating presentations, and even coding or managing projects. And when you look at B2B SaaS platforms with AI/ML capabilities, it’s clear that many entry-level roles are being automated. These tools are faster, cheaper, and often more efficient than hiring a fresher. We’ve seen similar disruptions before with the Industrial Revolution, the invention of the car, the rise of computers, and the internet. But this time, it’s not blue-collar trades being displaced. It’s young, college-educated professionals. And the shift is already underway. So, what can freshers do to stay relevant? 1. Upskill Continuously Learn to use AI tools relevant to your field. Take online courses in data analysis, digital marketing, coding, or project management on platforms like Coursera, edX, or LinkedIn Learning. 2. Build a Strong Network Use social media to connect with professionals, share your work, and engage with industry content. Attend webinars and virtual events to meet mentors and peers. 3. Focus on Soft Skills Critical thinking, communication, collaboration, and adaptability are more important than ever as these are skills AI can’t easily replicate. 4. Gain Experience Creatively Take on internships, freelance gigs, or volunteer roles. Start personal projects like blogs, apps, or YouTube channels to showcase your initiative. 5. Stay Informed Follow industry trends and understand how AI is reshaping your field. Read blogs, follow thought leaders, and subscribe to newsletters. The future of work is changing fast so let’s make sure we’re ready for it. Read the Fortune article - https://lnkd.in/gaGgCtFw #AI #FutureOfWork #Upskilling #JobMarket #LinkedInInsights #WorkplaceTrends Google OpenAI Anthropic