Career Options in Technology

If you’re AI-curious but can’t decide where to start, this one’s for you 👇 The AI space is vast. Buzzwords fly. Roles overlap. And it’s easy to get stuck wondering: 👉 Should I become a Data Scientist, ML Engineer, or Product Manager? Instead of chasing titles, map your strengths and figure out where you fit best in the AI lifecycle. 📌 I put together this infographic + a blog post to help you find your lane, with 10 clear roles you can actually train for (even without a PhD or a Stanford badge). 🚀 The 10 Career Paths in AI, Simplified: ➡️ AI/ML Researcher or Scientist – creating new algorithms, publishing papers, pushing the frontier ➡️ Applied ML Scientist / Data Scientist – solving real-world problems with models and experimentation ➡️ ML Engineer / MLOps / Software Engineer (ML) – taking models to production and scaling them ➡️ Data Engineer – building the infrastructure to move and manage data ➡️ Software Engineer – writing core product code with ML components ➡️ Data Analyst – analyzing data to drive insights and business impact ➡️ BI Analyst – working with KPIs, reporting, and decision frameworks ➡️ AI Consultant – advising teams and clients on adopting AI responsibly ➡️ AI Product or Program Manager – aligning AI capabilities with user needs and business goals ➡️ Hybrid Roles – wearing multiple hats across technical and strategic functions 🧭 How to choose the right one for you: → Start with your natural strengths: coding, communication, business thinking, or data sense → Identify the part of the AI lifecycle you enjoy most: research - build - deploy - iterate → Stack the right skills intentionally: • Coders: Python, PyTorch, prompt design, eval frameworks • Data Infra: SQL, Spark, Airflow, Lakehouse, vector DBs • Insights: Analytics, causal reasoning, dashboard tools • Translators: AI roadmap building, governance, storytelling → Focus on shipping evidence of work: demo apps, notebooks, open-source PRs, or experiments → Develop a T-shaped skill profile – go deep in one role, but stay conversational across others 💡 A few truths to keep in mind: → You don’t need to be a “10x coder” to work in AI → Problem-solving > job titles → Projects > perfect resumes → Cross-functional skills are a force multiplier – clear writing, ethical reasoning, and stakeholder empathy go a long way → There’s no “entry-level” in AI – just entry-level impact 📖 Curious to explore deeper? Check out the full blog, and save the infographic to use as a compass for your AI journey: https://lnkd.in/daQNHPyg

🚀 Embracing AI is not just enhancing your current role, but it can transform your next job While many discuss the immediate benefits of incorporating AI into existing jobs, let's explore how it paves the way for groundbreaking career transitions. 🏗️ From Civil Engineer to AI-Integrated Infrastructure Planner: Civil engineers using AI for structural analysis can become AI-Integrated Infrastructure Planners, designing future cities and structures with AI-augmented planning tools. 🎨 From Artist to Generative AI Agency Founder: As an artist, dive into AI image and text generation. This can be your stepping stone to launching a generative AI agency, pioneering in AI-powered art, music, and content creation. 📚 From Teacher to Personalized Learning Consultant: Teachers, use AI analytics for tailored student instruction. Your next leap? Become a consultant, guiding schools in adopting AI-driven personalized learning systems. 💻 From Data Analyst to MLOps Engineer: Data analysts, harness ML pipelines for data management and model deployment. Transition into MLOps, optimizing machine learning operations and workflows. 🤖 From Sales to Conversational AI Designer: Incorporating AI chatbots in sales? Use this experience to venture into designing advanced AI conversational interfaces. Why not start with a custom GPT model? 🔍 From HR to AI Recruitment Strategist: HR professionals, integrate AI in talent acquisition and employee engagement. Next, become a strategist in AI-enhanced recruitment, shaping the future of workforce management. 🏥 From Healthcare to AI Health Advisor: Healthcare workers using AI for patient care can transition to AI Health Advisors, guiding medical institutions in implementing AI for enhanced patient outcomes. 🔬 From Research Scientist to AI-Driven Drug Discovery Specialist: Research scientists involved in pharmaceuticals or biotech can leverage AI for faster, more accurate drug discovery. The next step? Become a specialist in AI-driven drug discovery, accelerating the development of life-saving medications. 🔑 The Key to Transition: Start small. Integrate AI as a tool to enhance your current role. Train models using no code tools, create with generative AI, or streamline processes. Hands-on AI experience broadens your capabilities, preparing you for the next career leap, and it will help you figure out where you want to go next. 💡 Takeaway: The key is finding small ways to sprinkle in AI as a tool that boosts your current role. Whether it's building ML models, using generative AI content, or streamlining workflows, hands-on AI experience can expand your capabilities. And that makes you more adaptable for whatever the next step in your career might be, especially as AI transforms more industries. So look for little ways to incorporate AI into your daily work - it could help unlock your next move. ------------------------------------ Follow Marily Nika, Ph.D for insights & Education on AI & Product Management

Traditional IT roles are becoming obsolete in the face of new innovations. The future of tech lies in roles that challenge classic job descriptions. It’s no longer just about keeping the lights on and systems running; it's about discovering and illuminating new paths. Let’s look into three cutting-edge IT roles that are transforming the tech landscape. 𝟯 𝗨𝗻𝗰𝗼𝗻𝘃𝗲𝗻𝘁𝗶𝗼𝗻𝗮𝗹 𝗜𝗧 𝗥𝗼𝗹𝗲𝘀 𝗥𝗲𝘀𝗵𝗮𝗽𝗶𝗻𝗴 𝘁𝗵𝗲 𝗧𝗲𝗰𝗵 𝗟𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲 𝟭. 𝗖𝗵𝗶𝗲𝗳 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗢𝗳𝗳𝗶𝗰𝗲𝗿 (𝗖𝗔𝗢) 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗱𝗼: A CAO spearheads the implementation of automation technologies, like AI-driven automation and robotic process automation (RPA), to streamline operations and boost efficiency. 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: Automation is the future. With a CAO, organizations can leverage these technologies to stay competitive and efficient. 𝙃𝙤𝙬 𝙩𝙤 𝘽𝙧𝙚𝙖𝙠 𝙄𝙣: Skills Needed: Knowledge of RPA tools, AI, machine learning, process improvement, change management. Background: Transition from process optimization, operations management, or IT project management. 𝘼𝙘𝙩𝙞𝙤𝙣 𝙎𝙩𝙚𝙥𝙨: ✔️Get certified in RPA and AI tools. ✔️Gain experience in process optimization projects. ✔️Develop strong change management skills. 𝟮. 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗘𝘁𝗵𝗶𝗰𝗶𝘀𝘁 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗱𝗼: A Digital Ethicist addresses the ethical implications of digital technologies and data usage, ensuring alignment with ethical standards and societal values. 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: In an age of data privacy concerns and ethical scrutiny, a Digital Ethicist helps maintain trust and compliance. 𝙃𝙤𝙬 𝙩𝙤 𝘽𝙧𝙚𝙖𝙠 𝙄𝙣: Skills Needed: Understanding of data privacy laws, ethics, social impact of technology, stakeholder engagement. Background: Ethics, law, social sciences, public policy. 𝘼𝙘𝙩𝙞𝙤𝙣 𝙎𝙩𝙚𝙥𝙨: ✔️Study data privacy laws and ethical guidelines. ✔️Engage in projects with social impact. ✔️Build strong stakeholder communication skills. 3. 𝙄𝙏 𝙑𝙖𝙡𝙪𝙚 𝙈𝙖𝙣𝙖𝙜𝙚𝙢𝙚𝙣𝙩 𝙎𝙥𝙚𝙘𝙞𝙖𝙡𝙞𝙨𝙩 In the comments 👇 𝙒𝙝𝙮 𝙏𝙝𝙚𝙨𝙚 𝙍𝙤𝙡𝙚𝙨 𝙈𝙖𝙩𝙩𝙚𝙧 Fresh Perspectives: These roles welcome professionals from varied backgrounds, injecting fresh perspectives into the IT field. Innovation and Competitive Advantage: Embracing these roles fosters innovation, keeping organizations ahead of the curve. Future-Proofing: With technology evolving, specialists in automation, digital ethics, and IT value management prepare organizations for future challenges. Imagine in 5-10 years when traditional IT roles are as obsolete as rotary phones. The future of IT is calling. Will you answer or let it go to voicemail?