Growth in tech requires stepping away from one strength to build another

Been scrolling through r/devops lately and the conversations feel pretty real. A lot of people are switching from full-stack roles (like MERN) into DevOps in this AI era. They're grinding tools for a month or two, building 10-15 projects, and asking the honest question: “What else do I actually need to land a job?” Threads about whether DevOps is still “worth it” in 2026 are everywhere some say yes because AI needs good pipelines and platforms behind it, others worry junior roles are shrinking. - Strong focus on fundamentals (Linux, Git, Terraform, Docker, K8s, observability) - Platform engineering gaining real traction to make developers’ lives easier - AI agents and self-healing stuff : exciting but people want practical wins, not just hype #DevOps #PlatformEngineering #AIOps #CareerInTech

💻 I used to think becoming an SDE was only about learning to code. Write programs. Solve DSA questions. Crack interviews. That’s what most of us believe at the start. But the more I explored real-world software development, the more I realized something important — code is only one part of the journey. Software isn’t finished when it runs on your laptop. It’s finished when real users can access it reliably. That’s when I discovered the importance of combining Software Development + DevOps. Here’s the roadmap I’m currently following 👇 --- 🧠 Start with strong fundamentals I focused first on understanding problem-solving instead of rushing into tools: • Data Structures & Algorithms • One main programming language • OOP concepts • Writing clean and understandable code Because tools change, but fundamentals stay. --- ⚙️ Learn how software actually works At some point, I realized good developers understand systems, not just syntax: • Operating Systems • DBMS & SQL • Computer Networks • APIs and backend basics Suddenly debugging started making sense instead of feeling random. --- 🌐 Build real projects (this changed everything) Tutorials helped me begin, but projects helped me learn: • Creating REST APIs • Authentication systems • Database design • Backend applications from scratch Nothing builds confidence like solving real problems. --- 🚀 Then came DevOps — the missing piece I started learning how software moves from code to production: • Docker for containerization • CI/CD pipelines • Cloud platforms like AWS/GCP • Kubernetes basics From “it works on my machine” → “it works for everyone.” --- ☁️ Thinking beyond coding Now I try to understand: • Deployment • Monitoring & logging • Linux basics • Scalability concepts Because real engineering starts after deployment. --- 🎯 My biggest realization: The industry is moving toward engineers who can build + deploy + maintain systems. Not just developers. Not just DevOps. But engineers who understand the full lifecycle of software. I’m still learning, still improving — and sharing the journey openly. If you’re also on this path, what are you learning right now? 👇 #SDE #DevOps #SoftwareEngineering #LearningInPublic #TechJourney #CloudComputing

🚀 Attended a powerful session on “Career Paths & Growth in DevOps & Software Development” by Scaler! Here are my key takeaways from the session 👇 🔹 1. Career Growth ≠ Just Money Focusing only on salary is a wrong goal. Instead, growth depends on: Skills Problem-solving ability Consistency in learning 🔹 2. Two Critical Questions To grow in tech, always ask: What to learn? How to learn? 🔹 3. Learning Approach Matters Learning alone vs with like-minded people → Completion rate improves with the right environment Hands-on learning is key (projects > theory) Clarity of path is more important than random learning 🔹 4. Career Paths in Tech Different paths based on interest & experience: Backend / Frontend / Full Stack Data Engineering DevOps AI/ML Engineering Analyst roles 🔹 5. Must-Have Foundations DSA & Problem Solving System Design (LLD + HLD) CS Fundamentals Real-world Projects 🔹 6. Tools & Platforms Recommended Practice: LeetCode, InterviewBit Learning: CodeHelp, Stanford/CMU resources DevOps/Cloud: AWS, Azure, Linux, Scripting AI/ML: Kaggle, Datacamp 🔹 7. Key Insight 👉 Strong projects + applied learning = real growth 👉 Pivoting is possible, but requires structured effort 💡 This session reinforced the importance of focused learning, hands-on practice, and choosing the right path based on individual goals. 🙏 Thanks to the Scaler team and the speaker for sharing such valuable insights! #Scaler #DevOps #SoftwareDevelopment #CareerGrowth #Learning #TechCareers #FullStack #DataEngineering #AI #MachineLearning #Cloud #AWS #Kubernetes #Docker #Programming #Developers #ContinuousLearning

🎯 Skills Every Software Engineer Should Learn in 2026 The tech landscape evolves fast. Here's what I'm focusing on: Core Skills (Must Have) ✅ Programming Language Mastery - Deep knowledge of 1-2 languages - Understanding language internals - Writing clean, maintainable code ✅ System Design - Scalable architecture patterns - Database design and optimization - API design and documentation ✅ DevOps & Cloud - CI/CD pipelines - Containerization (Docker, Kubernetes) - Cloud platforms (AWS, Azure, GCP) Emerging Skills (High Value) 🚀 AI/ML Integration - Using LLMs in applications - Understanding ML basics - Prompt engineering 🚀 Security - Secure coding practices - Authentication/authorization - Common vulnerabilities 🚀 Performance Optimization - Profiling and debugging - Caching strategies - Database optimization Soft Skills (Underrated) 💬 Communication - Technical writing - Presentation skills - Team collaboration 🧠 Problem Solving - Systematic approach - Debugging methodology - Root cause analysis 💡 Continuous learning is non-negotiable. The tech you learn today will be different tomorrow. What skill are you focusing on this year? #SoftwareEngineering #Skills #CareerDevelopment #TechSkills #Learning

🚀 “𝐈𝐬 𝐢𝐭 𝐭𝐨𝐨 𝐥𝐚𝐭𝐞 𝐭𝐨 𝐬𝐰𝐢𝐭𝐜𝐡 𝐢𝐧𝐭𝐨 𝐈𝐓?” This is one of the most common questions I hear. Recently, I had a 𝟏:𝟏 𝐬𝐞𝐬𝐬𝐢𝐨𝐧 that completely changed my perspective. I spoke with someone who had 𝟏𝟖 𝐲𝐞𝐚𝐫𝐬 𝐨𝐟 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 𝐚𝐬 𝐚 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐨𝐫 𝐢𝐧 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 👩🏫 But instead of staying in her comfort zone, she chose to follow her 𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭 𝐚𝐧𝐝 𝐭𝐫𝐚𝐧𝐬𝐢𝐭𝐢𝐨𝐧𝐞𝐝 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐈𝐓 𝐢𝐧𝐝𝐮𝐬𝐭𝐫𝐲. 👉 𝐒𝐡𝐞 𝐬𝐭𝐚𝐫𝐭𝐞𝐝 𝐡𝐞𝐫 𝐣𝐨𝐮𝐫𝐧𝐞𝐲 𝐢𝐧 𝐉𝐚𝐯𝐚 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 👉 𝐁𝐮𝐢𝐥𝐭 ~𝟒 𝐲𝐞𝐚𝐫𝐬 𝐨𝐟 𝐡𝐚𝐧𝐝𝐬-𝐨𝐧 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 𝐢𝐧 𝐭𝐡𝐞 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐝𝐨𝐦𝐚𝐢𝐧 During our session, we discussed how she can further strengthen her profile: 🔹 Deepening Java + Spring ecosystem knowledge 🔹 Working on Migration projects (real-world impact) 🔹 Gaining hands-on with SQL & NoSQL databases 🔹 Exploring DevOps tools (Jenkins, GitLab, Docker, Kubernetes) 🔹 Learning Cloud platforms (AWS / GCP / Azure) 🔹 Understanding how to stay relevant in the AI era 💬𝐀𝐟𝐭𝐞𝐫 𝐭𝐡𝐞 𝐬𝐞𝐬𝐬𝐢𝐨𝐧, 𝐡𝐞𝐫 𝐟𝐞𝐞𝐝𝐛𝐚𝐜𝐤 𝐭𝐫𝐮𝐥𝐲 𝐬𝐭𝐨𝐨𝐝 𝐨𝐮𝐭 𝐭𝐨 𝐦𝐞 (sharing a glimpse below 👇) What inspired me the most wasn’t just the feedback… It was her journey. ✨ 𝟏𝟖 𝐲𝐞𝐚𝐫𝐬 𝐢𝐧 𝐨𝐧𝐞 𝐜𝐚𝐫𝐞𝐞𝐫 ✨ 𝐂𝐨𝐮𝐫𝐚𝐠𝐞 𝐭𝐨 𝐬𝐭𝐚𝐫𝐭 𝐚𝐠𝐚𝐢𝐧 ✨ 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐲 𝐭𝐨 𝐠𝐫𝐨𝐰 𝐢𝐧 𝐚 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐥𝐲 𝐧𝐞𝐰 𝐝𝐨𝐦𝐚𝐢𝐧 💡 My biggest takeaway: 𝐈𝐭’𝐬 𝐧𝐞𝐯𝐞𝐫 𝐚𝐛𝐨𝐮𝐭 𝐚𝐠𝐞 𝐨𝐫 𝐲𝐞𝐚𝐫𝐬 𝐨𝐟 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞. 𝐈𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐭𝐡𝐞 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐭𝐨 𝐬𝐭𝐚𝐫𝐭 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐝𝐢𝐬𝐜𝐢𝐩𝐥𝐢𝐧𝐞 𝐭𝐨 𝐜𝐨𝐧𝐭𝐢𝐧𝐮𝐞. 𝗧𝗵𝗲𝗿𝗲 𝗶𝘀 𝗻𝗼 “𝗹𝗮𝘁𝗲” 𝗶𝗻 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴. 𝗧𝗵𝗲𝗿𝗲 𝗶𝘀 𝗼𝗻𝗹𝘆 “𝗻𝗼𝘁 𝘀𝘁𝗮𝗿𝘁𝗲𝗱 𝘆𝗲𝘁.” If you’re someone thinking about switching careers or upskilling—this is your sign. 🚀 #CareerSwitch #Motivation #SoftwareEngineering #Java #LearningJourney #GrowthMindset #DevOps #Cloud #AI #LinkedInCommunity

"𝗢𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲" started as someone couldn't fix a printer 🖨️ The story DevOPs engineer should know.. Today again I am bringing an exciting piece of #history. While exploring the journey of open source, I realized that the technology we use every day wasn’t built by big strategies.. it was built by small moments of frustration, curiosity, and belief. From a hacker at MIT who refused to accept closed code… to a student who casually wrote “just a hobby” and ended up powering the internet… to a room of 20 people who simply decided to call it “open source” None of them were trying to change the world. But they did. Key takeaway here is, Open source is not just about code being free. It’s about ideas being shared, improved, and trusted by strangers across the world. And somewhere along the way, it became the invisible foundation of everything we build today.. from DevOps pipelines to cloud infrastructure. There’s also a question.. If open source runs the world… who sustains the people building it? The carousel is my attempt to capture this journey, from its origins to its impact today. #AI assisted me to create it. It is phenomenal if you think of it. Isnt what we are learning here on #linkedin also part of that Open source culture of shared learning? Curious to hear your perspective 👇 What was your first experience with open source? #OpenSource #TechHistory #TechStorytelling #DevOps #Cloud #Learning DevOps Insiders #TechLeadership #LearningInPublic Ashish Kumar Aman Gupta

If I were starting my software engineering journey today, the roadmap I used five years ago would be obsolete. I remember my early days—the focus was purely on syntax and local environments. But after navigating technical workstreams at Google, architecting AI solutions at Microsoft, and building The CodeWolf from the ground up, the "traditional" path looks like a detour. The industry isn't just looking for "coders" anymore; it’s looking for AI-Native Cloud Architects. If I had to hit the reset button today, here are the 5 shifts I’d make to stay ahead: 1. Master "AI-Assisted Orchestration" Over Syntax 🤖 Earlier in my career, I spent hours debugging boilerplate. Today, I wouldn't. I'd treat GitHub Copilot and Cursor as my pair programmers from Day 1. The Shift: Focus on System Design and Prompt Engineering for code generation. Your value isn't in writing the for loop; it's in knowing why that loop belongs in a microservice. 2. Cloud-First, Not Local-First ☁️ I used to build everything on my machine, only to have it break during deployment. Today, I’d start directly in the cloud. The Shift: Instead of just learning Python, I’d learn how to deploy a Python function on Azure Functions or Google Cloud Run. Understanding "Serverless" and "Elasticity" early on is the difference between a junior dev and a solution engineer. 3. Move from "Data Storage" to "Data Intelligence" 📊 At Microsoft, I’ve seen how mission-critical AI is only as good as the data pipeline behind it. The Shift: I’d stop just learning SQL and start mastering Vector Databases (like Pinecone or Weaviate) and Databricks. In the AI era, knowing how to retrieve and feed data into an LLM via RAG (Retrieval-Augmented Generation) is a superpower. 4. Build a Public "Proof of Work" (The CodeWolf Strategy) 🐺 When I started my startup, The CodeWolf, I realized that a resume is just a piece of paper, but a GitHub repo and a technical blog are a "Proof of Work." The Shift: I’d document my learning in public. Every time I solve a cloud scaling issue or a deployment bug, it goes on LinkedIn or a personal blog. Impact: This builds a "gravity" that pulls opportunities to you, rather than you chasing them. 5. Prioritize "Agentic" Thinking 🧠 We are moving from models that chat to agents that act. The Shift: I’d spend less time on basic web dev and more time understanding frameworks like LangChain or Semantic Kernel. Building apps that can browse the web, execute code, and make decisions is the next frontier. 💡 The Hard Truth The "safe" path of learning just one language and staying in your lane is gone. Whether I was at Google or now at Microsoft, the most successful engineers are those who view themselves as Problem Solvers first and Technologists second. The tools will change (and they'll change fast), but the ability to architect intelligent, distributed systems is a timeless skill. To my fellow engineers: If you were starting today, what’s the one thing you’d do differently? 👇 #

₹0 → ₹25 LPA with Kubernetes. Sounds unrealistic? It’s not. I’ve seen this happen multiple times. But not randomly. There’s a pattern 👇 Stage 1 → Confusion 😵 • Watching random tutorials • No clear direction • No real progress Stage 2 → Foundation 🧱 • Linux basics • Docker understanding • First Kubernetes concepts Stage 3 → Hands-on 💻 • Deploy apps • Break things • Fix them Stage 4 → Certification ☸️ • CKAD / CKA • Structured learning • Real validation Stage 5 → Real-world thinking 🧠 • Debugging • Scaling • Production mindset This is where everything changes. Because now you’re not: ❌ Learning Kubernetes You’re: ✅ Thinking like an engineer And that’s what companies pay for. Here’s the biggest mistake: ❌ Trying to skip steps Everyone wants: 👉 Salary jump But ignores: 👉 Skill building That’s why most people stay stuck. The path is simple: 👉 Follow the process 👉 Stay consistent 👉 Execute daily Do this for 6–9 months… And your career trajectory changes. So ask yourself: Which stage are you in right now? Let’s discuss 👇 💡 Comment “K8S” and I’ll share a complete roadmap. 👉 Follow me for daily Kubernetes + DevOps growth content 🚀 #Kubernetes #DevOps #CKA #CKAD #CKS #CloudComputing #KubernetesEngineer #SalaryGrowth #CloudCareers #DevOpsEngineer #TechCareers #CloudGuru #CareerGrowth #LinuxFoundation 🚀

In the era of GenAI, which language should I learn - Python or Go? An interesting question from one of my DevOps engineers. At first glance, it sounds like a straightforward choice: Go powers much of the modern cloud-native ecosystem (Kubernetes, Docker, Terraform…) Python has been the backbone of automation, scripting, and now AI/ML But the real answer is a bit uncomfortable: 👉 The language you choose matters less than how you think about building software. The Shift We’re Living Through With LLMs like Claude Sonnet or Opus, generating code is no longer the bottleneck. You can: - Scaffold a REST API in seconds - Generate Terraform modules - Write Kubernetes operators - Automate workflows So if code generation is becoming commoditized… 👉 What actually differentiates engineers going forward? What Still Matters (More Than Ever) 1. Understanding Trade-offs Knowing why Go is used for infrastructure tools: - Concurrency model (goroutines, channels) - Static binaries (ease of distribution) - Performance and low memory footprint Knowing why Python dominates automation: - Rich ecosystem - Faster prototyping - Simplicity and readability AI can generate both but it won’t deeply understand your system constraints unless you do. 2. System Design Thinking Can you answer: - Should this be a long-running service or a batch job? - When do you use event-driven vs polling? - Where does the state live? - How does this scale under failure? These decisions are language-agnostic and AI won’t get them right without strong guidance. 3. Code Quality & Maintainability Generated code often works… until it doesn’t. The real skill is: - Structuring codebases - Applying design patterns appropriately - Writing testable, observable systems - Managing dependencies and versioning In DevOps especially, “quick scripts” often become “critical systems” overnight. 4. Understanding the Runtime Especially in platform engineering: - How does garbage collection impact latency? - What happens under high concurrency? - How do network calls behave under failure? This is where Go shines but only if you understand it beyond syntax. 5. Operational Thinking As DevOps engineers, we don’t just write code, we run it. - Observability - Failure modes - Cost implications - Deployment patterns AI can write code. It cannot own production (yet). The Real Answer Don’t optimize for language choice. Optimize for engineering depth. In a world where AI writes code: - Syntax is cheap - Judgment is expensive The engineers who will stand out are the ones who can: - Ask the right questions - Design the right systems - Validate and evolve solutions over time #DevOps #PlatformEngineering #SoftwareEngineering #CloudNative #Kubernetes #Golang #Python #GenerativeAI #LLM #AICoding #EngineeringLeadership #TechCareers #CareerGrowth #LearningToLearn #SystemDesign #CleanCode #EngineeringExcellence

Docker changed how we build and ship software, especially in microservices systems. Earlier, most problems were not in code but in environments. Different setups across development, testing, and production caused failures that were hard to debug. Deployments were slow, dependencies conflicted, and scaling systems required heavy virtual machines and manual effort. Docker solved this by introducing containers. A container packages the application along with its runtime and dependencies, making it portable and consistent. If it runs once, it runs the same everywhere. In microservices architecture, this becomes very powerful. Each service runs in isolation, avoiding conflicts and enabling independent deployments. Scaling becomes straightforward by running multiple instances of the same container. CI/CD pipelines also improve because the same image moves across environments without change. In real systems, this leads to faster deployments, fewer environment-related issues, and better developer productivity. However, Docker is not a complete solution. It does not fix poor system design or remove the need for monitoring and logging. It is a foundation, not the entire system. For anyone learning backend or system design, understanding Docker is important. Focus not just on commands, but on the problem it solves and how it fits into modern architectures. #Docker #softwareengineer #sde #recruiter #ai #hiring #devops #aws #learning