Jamal KHINDOUF

𝐀𝐈 + 𝐃𝐞𝐯𝐎𝐩𝐬 = 𝐓𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐲 DevOps is evolving fast-and AI is becoming the secret weapon behind smarter, faster, and more resilient operations. 𝐓𝐡𝐚𝐭 𝐢𝐬 𝐰𝐡𝐲 𝐈 𝐜𝐫𝐞𝐚𝐭𝐞𝐝 𝐭𝐡𝐢𝐬 𝐀𝐈 𝐂𝐡𝐞𝐚𝐭𝐬𝐡𝐞𝐞𝐭 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 👉 (𝐬𝐞𝐞 𝐠𝐫𝐚𝐩𝐡𝐢𝐜) 💡 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐢𝐧𝐬𝐢𝐝𝐞: ✅ AI in DevOps explained (CI/CD, infra, monitoring, security) ✅ Intelligent infrastructure & scaling (auto-scaling, GPU scheduling, optimization) ✅ AI-Enhanced CI/CD use cases (predicting failures, anomaly detection, code quality analysis) ✅ AI-powered monitoring layers (infra, logs, app performance) ✅ Multi-Agent DevOps (autonomous agents collaborating across pipelines) ✅ Security & compliance (zero-day exploits, policy audits, GDPR/HIPAA enforcement) ✅ Quick tips + tools to get started 🔥 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲: AI does not just speed up deployments-it enables predictive incident management, auto-scaling, continuous optimization, and enhanced security. The future of DevOps will be multi-agent, intelligent, and autonomous. 𝐀𝐫𝐞 𝐲𝐨𝐮 𝐩𝐫𝐞𝐩𝐚𝐫𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐟𝐨𝐫 𝐭𝐡𝐢𝐬 𝐬𝐡𝐢𝐟𝐭? ♻️ Repost this to help your network get started ➕ Follow Jaswindder Kummar for more #DevOps #AICheatsheet #AgenticAI #Cloud #AIForDevOps #Automation

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🚀 Life of a Cloud Engineer – Expectations vs Reality 🌩️ Many think Cloud Engineers just “catch clouds and put them in boxes” ☁️📦… But in reality, it’s about: ✅ Designing scalable architectures ✅ Managing servers & networks ✅ Implementing CI/CD pipelines ✅ Ensuring security & cost optimization It’s not just about the *cloud*, it’s about building the backbone of modern businesses. #CloudComputing #CloudEngineer #DevOps #AWS #Azure #GoogleCloud #EngineeringLife #TechCommunity #DigitalTransformation #Innovation #FutureOfWork

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Why Many DevOps CVs Fail to Stand Out — and How to Fix Yours After years of seeing how DevOps roles are advertised, discussed, and filled, one thing is clear: too many CVs sound exactly the same. The Usual “Tool List” Approach: I. Docker & Kubernetes experience II. CI/CD pipeline implementation III. Infrastructure as Code (IaC) IV. Monitoring & observability V. Cloud platforms (AWS, Azure, GCP) In 2025, listing these without context is like writing “Proficient in Microsoft Word.” What Gets Noticed Instead: I. Specific results: “Reduced deployment time from 4 hours to 8 minutes.” II. Real problems solved: “Eliminated 3 AM alerts by adding automated health checks.” III. Business impact: “Improved system uptime from 99.9% to 99.99%, preventing £50k in lost sales.” IV. Learning from challenges: “Introduced chaos engineering after a major outage.” V. Sharing knowledge: “Built an internal DevOps onboarding program for new hires.” The “Show, Don’t Tell” Principle: Add a link to your GitHub with actual infrastructure code. Publish a short write-up about a technical challenge you solved. Highlight open-source contributions. Share talks or presentations you’ve delivered. Example of a Stand-Out CV Line: "Built a deployment pipeline allowing a small team to ship 40+ releases/week without weekend work." That shows automation skills, a focus on efficiency, and the ability to scale both systems and teams. Takeaway: Don’t just list the tools you’ve touched. Tell the story of your impact. That’s what employers and collaborators remember. — Chigozie Ozoemena #StackedByChigozie AWSWithChigozie

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⎈ 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐈𝐧𝐠𝐫𝐞𝐬𝐬 𝐌𝐚𝐬𝐭𝐞𝐫𝐲 – Complete Guide for DevOps Engineers ╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍ From Zero ➝ Production, everything a DevOps engineer needs to know: ✅ Real-world examples ✅ CI/CD Integration ✅ Controller Comparison ✅ Best Practices A clear and structured guide to simplify service exposure and master Ingress in production. ╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍ ♻️ If this inspires you, share it with your network! 🔴 Follow me for more #Kubernetes & #DevOps 👉 https://lnkd.in/deugUkHT ╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍ #Kubernetes #DevOps #CloudNative #CKA #CKAD #CKS #CI/CD

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🔥 Interview Experience – Tech Lead / Architect Level (Spring Boot & Microservices) – Part 2 In my previous post, I have shared some core Java interview areas that were discussed. This time, let’s dive into the Spring Boot & Microservices side 🚀 The conversations went much deeper than just annotations – the focus was on “why this design choice?” and “how does it play out in real-world systems?” Here are some of the major topics that came up: ✨ REST API Design & Best Practices Difference between a true REST API vs a plain HTTP API REST Principles – Statelessness, Uniform Interface, Cacheability, Layered System, HATEOAS Best practices – versioning, idempotency, proper status codes, pagination, rate limiting ⚡ Circuit Breaker & Resilience4j How to build fault-tolerant microservices Fallback vs retry – when to choose which 🔀 Content Negotiation Returning JSON/XML/custom types How Spring Boot resolves “Accept” headers 🛠️ @Component vs @Bean Declarative vs programmatic bean registration 🏗️ @Component vs @Configuration Why @Configuration uses CGLIB proxying and its impact 🌀 @Transactional Transaction boundaries, propagation levels, common pitfalls 🌍 @CrossOrigin Handling CORS securely in production 📌 Spring MVC Architecture DispatcherServlet → HandlerMapping → Controller → ViewResolver flow ♻️ Circular Dependency Why it happens, and fixes with @Lazy, setter injection, or redesign 🔐 Spring Security JWT vs OAuth2 vs SSH – when to use what Method Level Security - @PreAuthorize @PostAuthorize 📊 Application Monitoring & Observability Why monitoring is critical in microservices Tools discussed: Prometheus, Grafana, Micrometer, ELK Stack, Zipkin (for tracing) Interviewers often dig into: “How would you detect a production issue before the customer reports it?” 💡 Key takeaway: At this level, knowing annotations/config isn’t enough. You need to explain the why, when, and trade-offs behind each choice. 👉 Coming up in Part 3 – System Design & Cloud-native patterns: API Gateway vs ALB, Event-driven systems with Kafka, Caching with Redis, and more. 🎯 Now I’d love to hear from you: 📌 What’s the toughest Spring Boot/Microservices question you’ve faced in an interview? 📌 Which monitoring tool do you prefer in production – Prometheus + Grafana, NewRelic, or something else? #SpringBoot #Microservices #Java #InterviewPreparation #SystemDesign #Observability #SoftwareArchitecture

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Why is Everyone Suddenly Trying to Become a DevOps Engineer? Because truth be told… DevOps is no longer just a role – it’s the backbone of modern software delivery: speed, reliability, and impact. Let’s break it down: ● No need to be a hardcore developer, but still deeply involved in tech ● Automation + Cloud + Problem-solving > Degrees & Certifications ● One of the few roles where you touch code, infra, CI/CD, and business outcomes ● Path to SRE / Cloud Architect / Platform Engineer ● Remote-friendly, future-proof, cross-domain flexible And most importantly… You don’t just deploy software – you help ensure it runs flawlessly in production. But Here’s What People Don’t See: ● 2 AM on-call alerts ● Pipelines breaking minutes before release ● Balancing speed with security & compliance ● Developers vs Ops tug-of-war (you’re in the middle) ● All eyes on you when production goes down… Because a DevOps Engineer is expected to automate the gaps and keep the system alive. So if you ask: “Why is everyone shifting to DevOps roles?” It’s simple: It’s where technology meets responsibility.

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