Tech Skills for Future Jobs

When I first stepped into the world of cybersecurity, I was completely lost. I didn’t know where to start, what to learn first, or how people even got into this field. All I knew was—I wanted to be a part of this world where people protect, investigate, and defend against digital threats. 💻⚡ At first, everything looked complicated: hacking, tools, reports, and those mysterious terms like “VAPT” and “SOC.” But slowly, I realized that becoming a cybersecurity professional isn’t about learning everything at once—it’s about building layer by layer. So here’s how the journey begins 👇 📍 Step 1: Build your base Understand the fundamentals — Computer basics, Networking, Linux, Windows, and a bit of Programming. This is your foundation. Without it, cybersecurity concepts won’t make sense. 📍 Step 2: Explore the world of security Learn about Web Security, System Security, Network Security, Cryptography, and Cybersecurity Fundamentals. Then dive deeper into areas like VAPT, Incident Response, Digital Forensics, and Cloud Security. 📍 Step 3: Play and practice This is where learning gets fun! Platforms like TryHackMe, HackTheBox, PortSwigger Academy, OverTheWire, VulnHub, and LetsDefend are your playgrounds. Each challenge you solve teaches you real-world skills. 📍 Step 4: Find your direction You can become a Security Analyst, SOC Technician, Penetration Tester, Threat Intelligence Analyst, or even a Cloud Security Associate ☁️ Each path has its own tools, techniques, and challenges. 📍 Step 5: Prepare for your career Start building projects, upload your reports to GitHub, and prepare at least three pentest reports. Add certifications like CompTIA Security+, CEH, or OSCP. And don’t forget to network on LinkedIn — it opens doors you didn’t even know existed. 🤝 🔥 My advice? Start small, stay consistent, and document everything you learn. Cybersecurity isn’t just about hacking—it’s about protecting, analyzing, and defending. 💪 So if you’re someone who’s confused, just like I was—this roadmap is your compass. Let’s build the next generation of ethical hackers and defenders together. 💣 If you’d like resume guidance, just DM me your “RESUME.” And for more such content, follow my channel: 👉 https://lnkd.in/gGAnR_UF #CyberSecurity #EthicalHacking #InfoSec #TryHackMe #HackTheBox #VAPT #PenTesting #DigitalForensics #SOC #IncidentResponse #BlueTeam #RedTeam #BugBounty #NetworkSecurity #CloudSecurity #Linux #CompTIA #CEH #OSCP #SecurityAnalyst #CyberCareer #CybersecurityCommunity #CyberAwareness #TechCareers #CyberInternship #CyberLearning #InfosecJourney

No matter how good you are in your current role, always try to do this once in your career: Start a project from scratch and implement the foundations correctly. I have seen rockstars coders who shivers when they have to start something from scratch. Because it’s only when you build something end-to-end that you understand the real-world challenges. Here are a few things you should definitely try: - Implement authentication (JWT, OAuth, sessions, etc.) - Set up a database from zero (create schema, auth tables, migrations) - Host your code on GitHub/GitLab/Bitbucket with branch protection rules - Configure environments (dev, staging, prod) and secrets properly - Write unit, integration & end-to-end tests - Set up a CI/CD pipeline for automated testing & deployments - Design APIs with proper documentation (Swagger/OpenAPI) - Apply security best practices (validation, HTTPS, rate limiting, CORS) - Containerize your app with Docker - Deploy once on a cloud provider (AWS, Azure, GCP, Render, etc.) - Add proper logging & error monitoring (Sentry, ELK, etc.) - Use caching (Redis, Memcached) for performance - Maintain code quality with linters, formatters, and static analysis - Write a clear README and contribution guide You don't need to go deep in any of these, just do till the point you can at least start from a blank slate. Once you’ve gone through this exercise, you’ll never look at your day-to-day work the same way again, you’ll know why things are set up the way they are, and what it takes to ship a project end-to-end. #project #scratch #basics

🔮 "AI and Big Data aren’t just trends — they’re the backbone of tomorrow’s economy." By 2030, the most valuable skillset won’t be just technical — it’ll be adaptable. Are you ready? According to the World Economic Forum’s Future of Jobs Report 2025, AI and Big Data skills are projected to see an 87% net increase in demand globally by 2030 India, with its rapidly expanding digital economy, is uniquely positioned to capitalize on this transformation. ▶️ 𝗜𝗻𝗱𝗶𝗮’𝘀 𝗧𝗲𝗰𝗵 𝗧𝗿𝗮𝗷𝗲𝗰𝘁𝗼𝗿𝘆:  • The Indian tech industry is targeting $500 billion in revenue by 2030   • Demand for AI, Big Data, and Cybersecurity specialists is expected to grow by over 60%.  • Nearly 1 million young Indians enter the workforce every month — a demographic dividend that can become a global advantage if upskilled effectively. 📈 𝗧𝗼𝗽 𝗘𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝗥𝗼𝗹𝗲𝘀 𝗶𝗻 𝗜𝗻𝗱𝗶𝗮:  • Big Data Specialists: Critical to managing the explosion of data across industries.  • AI & Machine Learning Specialists: Driving automation, personalization, and innovation.  • Security Management Specialists: Safeguarding complex digital ecosystems. 🧠 𝗞𝗲𝘆 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝘁𝗼 𝗙𝘂𝘁𝘂𝗿𝗲-𝗣𝗿𝗼𝗼𝗳 𝗧𝗵𝗲𝗶𝗿 𝗖𝗮𝗿𝗲𝗲𝗿𝘀:  • Big Data Tools (Spark, Hadoop, Kafka)  • AI & ML Integration (MLOps, model deployment)  • Cloud Computing (AWS, Azure, GCP)  • Cybersecurity Awareness  • Analytical & Creative Thinking  • Technological Literacy & Agility 💡 𝗦𝗼𝗳𝘁 𝗦𝗸𝗶𝗹𝗹𝘀 𝗠𝗮𝘁𝘁𝗲𝗿 𝗧𝗼𝗼:  • Resilience  • Flexibility  • Systems Thinking  • Collaboration across disciplines 🌍 Why This Matters: With 39% of core job skills expected to change by 2030  2, the future belongs to those who can adapt, learn, and lead in a tech-first world. Data Engineering isn’t just surviving — it’s evolving into one of the most strategic and high-impact roles of the next decade. 📘 Dive into the full World Economic Forum here: https://lnkd.in/gMExtKHr #Data #Engineering #AI #BigData

As AI reshapes work, the skills that matter most are shifting rapidly. Today we published our sixth annual In-Demand Skills report for 2026. It shows demand for AI-specific skills grew 109%, with surges in areas like AI video editing (+329%) and AI integration (+178%). Let’s put this in context. Approximately 85% of jobs that exist today didn’t exist in 1940. There are huge fields of work today that literally could not have been imagined in earlier eras because the platforms that they are predicated on did not exist. At that time, there were zero computer programmers, web developers, or solar panel installers. Jobs like app developer, social media manager, drone operator, cloud architect, and podcast producer could not exist because of the lack of the underpinning technology for those jobs. With the emergence of new AI technologies, we are now entering a ten-year period that will be marked by massive transformation of work yet again. Our data highlights how fast-moving and widespread business demand is for AI talent. And not just talent that can use AI tools – employers are placing a new premium on uniquely human skills that AI cannot displace. Nearly half of business leaders now say they'll pay a premium for creativity, innovation, and judgment – critical skills that turn AI outputs into business results. Traditionally full-time roles are also splintering, as AI is introduced: 77% of executives say AI is increasing their need for fractional, on-demand talent rather than traditional full-time roles. The tectonic plates of work are moving under our feet. On Upwork, we're leaning into this shift: supporting small, mid-sized, and enterprise businesses looking for highly skilled fractional talent and hiring for AI specialists who can own end-to-end results. To see how this shows up across the most in-demand skills, check out our new research below in comments.

A few weeks ago, a VP asked me why I love AWS so much. The question stopped me in my tracks. We were talking about how I moved from Australia to NYC for this role, all the AWS content I created before they ever paid me, and I realised that I'd never actually said this out loud. My answer: class mobility! Here's what I mean: AWS lets anyone with an internet connection and the willingness to learn build the same things that used to require a heap of venture capital, an ivy league CS degree, or knowing the right people. Free tier, documentation, some blogs and video tutorials - that's all you need to start building production systems that can scale to millions of users. That changes everything about who gets to build the future. I'm one generation off a sheep farm - I didn’t grow up with tech connections or any idea this was a career path. I dropped out of a masters in financial mathematics, taught myself Python from docs on nights and weekends, learned AWS through the free tier and breaking things, with some certifications along the way. The same infrastructure that powers so much of the internet was available to me in regional Australia for free (if I remembered to turn billing alerts on!). I could learn by doing, building real things, proving I could do the work - without asking anyone's permission or going into massive amounts of debt for another full degree. Now I'm on stages in Manhattan. That's class mobility. I've watched so many others do this. Single parents learning cloud after the kids go to bed. People without high school diplomas getting solutions architect roles. Folks from small towns building global SaaS companies. AWS didn't invent ambition or talent - those were always there. But it removed the gatekeepers. That's why I love it. So - what’s your reason? 👩🏻💻 Follow me (Brooke Jamieson) to stay in the loop with the latest AWS + AI updates for developers 📍 save + share! 🏷️ #AWSCommunity #TechCareers #HereatAWS Amazon Web Services (AWS)

What if the most in-demand jobs of 2026 aren’t defined by title—but by how humans think in an AI-powered world? LinkedIn’s workforce data shows significant growth in AI-connected roles: AI Engineers, Workflow Automation Specialists, ML Ops, Cybersecurity, Data Governance, and roles focused on managing or interpreting AI-generated output. But here’s the trend behind the trend—and it’s what I’m seeing firsthand in executive coaching: ➤ As AI capability rises, human judgment becomes the differentiator. McKinsey & Company reports that demand for analytical thinking, social-emotional skills, and adaptability is increasing as fast as demand for technical ability. That gap shows up every week in leadership conversations I’m part of. AI may change job titles. But it doesn’t change what organizations truly need -- people who can question assumptions, interpret complexity, and lead others through uncertainty. If you want to stand out in a volatile job market, try this: 🔹 Build AI literacy so you understand how tools shape decisions 🔹 Strengthen critical thinking—don’t accept outputs at face value 🔹Demonstrate curiosity and adaptability when the answers aren’t clear The jobs on the rise reward speed. The careers on the rise reward Human Intelligence. #JobsOnTheRise

Most people think RAG is just “connect an LLM to a bunch of documents and start asking questions.” That’s the naive version. Production RAG looks nothing like that. When you actually take a RAG system to production, you realize how many moving parts are hidden underneath. It’s not just documents sitting in one folder. You’re often dealing with multi-modal data (text, images, tables, PDFs, audio transcripts) and data sources scattered across multiple places. Your data warehouse, internal wikis, vector DBs, third-party APIs, customer support tickets, the list keeps going. The real engineering work shows up in the optimization layer: → Planning your retrieval strategy before you write a single line of code → Choosing the right embedding model for your domain (default is rarely the best) → Getting the balance right between semantic search, keyword search, and vector search → Adding re-ranking so the top-k results are actually the most relevant ones → Thinking carefully about chunking strategy, metadata filters, deduplication, and hybrid retrieval And that’s before you even touch evaluation, monitoring, cost control, and observability. I put together this handout covering the foundations of building production grade RAG systems. From ingestion and chunking, to retrieval, augmentation, evaluation, and the common pitfalls people hit the moment they ship. If you’re getting started in AI engineering, treat this as your checklist. Keep it top of mind as you build. To be clear, these are the foundations. Not advanced techniques. Master these first before you start layering on things like agentic retrieval, query routing, or self-correcting RAG loops. Save this one. You’ll come back to it 🫶

I've been building and deploying RAG systems for 2+ years. And it's taught me optimizing them requires focusing on 3 core stages: 1. Pre-Retrieval 2. Retrieval 3. Post-Retrieval Let me explain - Most people focus on the generation side of things. But optimizing retrieval is what really makes the difference. Here's how to do it: 𝟭/ 𝗣𝗿𝗲-𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 This is where we optimize the data before the retrieval process even begins. The goal? Structure your data for efficient indexing and ensure the query is as precise as possible before it's embedded and sent to your vector DB. Here’s how: - 𝗦𝗹𝗶𝗱𝗶𝗻𝗴 𝘄𝗶𝗻𝗱𝗼𝘄: 𝘐𝘯𝘵𝘳𝘰𝘥𝘶𝘤𝘦 𝘤𝘩𝘶𝘯𝘬 𝘰𝘷𝘦𝘳𝘭𝘢𝘱 𝘵𝘰 𝘳𝘦𝘵𝘢𝘪𝘯 𝘤𝘰𝘯𝘵𝘦𝘹𝘵 𝘢𝘯𝘥 𝘪𝘮𝘱𝘳𝘰𝘷𝘦 𝘳𝘦𝘵𝘳𝘪𝘦𝘷𝘢𝘭 𝘢𝘤𝘤𝘶𝘳𝘢𝘤𝘺. - 𝗘𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 𝗴𝗿𝗮𝗻𝘂𝗹𝗮𝗿𝗶𝘁𝘆: 𝘊𝘭𝘦𝘢𝘯, 𝘷𝘦𝘳𝘪𝘧𝘺, 𝘢𝘯𝘥 𝘶𝘱𝘥𝘢𝘵𝘦 𝘥𝘢𝘵𝘢 𝘧𝘰𝘳 𝘴𝘩𝘢𝘳𝘱𝘦𝘳 𝘳𝘦𝘵𝘳𝘪𝘦𝘷𝘢𝘭. - 𝗠𝗲𝘁𝗮𝗱𝗮𝘁𝗮: 𝘜𝘴𝘦 𝘵𝘢𝘨𝘴 (𝘭𝘪𝘬𝘦 𝘥𝘢𝘵𝘦𝘴 𝘰𝘳 𝘦𝘹𝘵𝘦𝘳𝘯𝘢𝘭 𝘐𝘋𝘴) 𝘵𝘰 𝘪𝘮𝘱𝘳𝘰𝘷𝘦 𝘧𝘪𝘭𝘵𝘦𝘳𝘪𝘯𝘨. - 𝗦𝗺𝗮𝗹𝗹-𝘁𝗼-𝗯𝗶𝗴 (or parent) 𝗶𝗻𝗱𝗲𝘅𝗶𝗻𝗴: 𝘜𝘴𝘦 𝘴𝘮𝘢𝘭𝘭𝘦𝘳 𝘤𝘩𝘶𝘯𝘬𝘴 𝘧𝘰𝘳 𝘦𝘮𝘣𝘦𝘥𝘥𝘪𝘯𝘨 𝘢𝘯𝘥 𝘭𝘢𝘳𝘨𝘦𝘳 𝘤𝘰𝘯𝘵𝘦𝘹𝘵𝘴 𝘧𝘰𝘳 𝘵𝘩𝘦 𝘧𝘪𝘯𝘢𝘭 𝘢𝘯𝘴𝘸𝘦𝘳. - 𝗤𝘂𝗲𝗿𝘆 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: 𝘛𝘦𝘤𝘩𝘯𝘪𝘲𝘶𝘦𝘴 𝘭𝘪𝘬𝘦 𝘲𝘶𝘦𝘳𝘺 𝘳𝘰𝘶𝘵𝘪𝘯𝘨, 𝘲𝘶𝘦𝘳𝘺 𝘳𝘦𝘸𝘳𝘪𝘵𝘪𝘯𝘨, 𝘢𝘯𝘥 𝘏𝘺𝘋𝘌 𝘤𝘢𝘯 𝘳𝘦𝘧𝘪𝘯𝘦 𝘵𝘩𝘦 𝘳𝘦𝘴𝘶𝘭𝘵𝘴. 𝟮/ 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 The magic happens here. Your goal is to improve the embedding models and leverage DB filters to retrieve the most relevant data based on semantic similarity. - Fine-tune your embedding models or use instructor models like instructor-xl for domain-specific terms. - Use hybrid search to blend vector and keyword search for more precise results. - Use GraphDBs or multi-hop techniques to capture relationships within your data. 𝟯. 𝗣𝗼𝘀𝘁-𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 At this stage, your task is to filter out noise and compress the final context before sending it to the LLM. - Use prompt compression techniques. - Filter out irrelevant chunks to avoid adding noise to the augmented prompt (e.g., using reranking) 𝗥𝗲𝗺𝗲𝗺𝗯𝗲𝗿: RAG optimization is an iterative process. Experiment with various techniques, measure their effectiveness, compare them and refine them. Ready to step up your RAG game? Check out the link in the comments.

Cybersecurity Career Tips #1 If you want to enter the cybersecurity field, it’s not enough to just pick a list of courses, complete them, generate certificates, and think the job will come naturally. And it’s definitely not just about adding certifications to your resume that’s only one step in the process. It’s essential to learn what is applied in real work contexts. You don’t need to study C if you’ll never use it in your daily tasks. Your studies should be aligned with your actual needs. My first recommendation if you want to become a cybersecurity professional is to understand what the market is looking for. Analyze open positions in your region or remote roles, define the requirements for each position, and identify the practical skills you need. Platforms such as HackTheBox, TryHackMe, PortSwigger Academy, PentesterLab, and Root-Me are excellent for hands-on learning. I strongly recommend investing your time in acquiring real-world skills. Write write-ups, share your journey here on LinkedIn or other networks, build personal projects and publish them on GitHub, connect with other professionals, and expand your network both online and at industry events. Also, develop your soft skills. Communication is critical, even in a job interview. Being able to translate technical issues into business impact is just as important as technical knowledge. A common way to start a career is by working in consulting firms. There are many opportunities at different seniority levels. It may not be your dream job, but it opens doors. Prepare your resume for the positions you aim for and highlight the key points that match the role especially if specific knowledge is required. A resume will only be considered if it demonstrates the right skills, relevant training or certifications (to validate your expertise), and professional autonomy. And don’t limit your job search to LinkedIn. It’s great for networking, but when it comes to landing jobs, explore alternatives. Target companies that interest you and check their career pages many positions are never posted on LinkedIn. Above all, stay focused. Don’t try to learn everything at once. Concentrate on what will land you your first job, and then expand your knowledge base to increase your seniority or pivot to other areas. But the real secret lies in how you communicate and sell your work your knowledge, your problem-solving mindset, and your ability to handle situations consistently. #CyberSecurity #InfoSec #CareerAdvice #Hacking #TechJobs #SoftSkills

I used to treat DevOps and Cloud Engineering as two separate career paths. Two different job titles. Two different skill sets. Two different types of engineers. But the more real-world systems I’ve worked with, the more I realized something: They’re not separate at all. They’re two sides of the same discipline — companies only split them because they can’t find engineers who can do both. Here’s what shifted my perspective: Knowing AWS but not automating anything turns you into a manual cloud operator. You’re basically a human console. Knowing automation but not understanding cloud architecture means you’re optimizing the wrong things. You can build pipelines all day, but the system won’t scale if the foundations are fragile. The real leverage happens when you combine them: Lambda + CI/CD → Deployments in seconds, not hours S3 + IaC → Infrastructure that can be rebuilt with a single command DynamoDB + automation → Databases that scale without engineers touching anything IAM + policy automation → Security that enforces itself CloudWatch + custom tooling → Systems that tell you what’s wrong before users notice This is why the companies paying $150K–$200K+ aren’t really searching for “Cloud Engineers” or “DevOps Engineers.” They’re looking for the people who bridge the gap: Between infrastructure and delivery Between automation and architecture Between reliability and velocity Because when one engineer can design the platform and automate how it’s built, tested, secured, deployed, and observed — that’s where the real value is. That’s the skill set I’m focusing on: Becoming the engineer who makes infrastructure invisible and delivery effortless. If you’re on the same path, keep going. The future belongs to the engineers who understand the full lifecycle — not just one half of it.