Top Skills Needed for Software Engineers

Dear software engineers, you'll definitely thank yourself later if you spend time learning this today: ⥽ Redis  > Your AI tools can help you write code, but when it comes to fast data access and handling spikes, Redis knowledge will save you in prod. ⥽ Docker & Kubernetes > Knowing how to build, ship, and scale with Docker and K8s will make you valuable even if you’re using Copilot for code. ⥽ Message Queues (Kafka, RabbitMQ, SQS, etc.)  > When you need to decouple services or handle unpredictable spikes, nothing beats message queues. AI will not explain why your system dropped messages at 3AM, but you’ll know if you actually practiced queues. ⥽ ElasticSearch  > Search is not just about matching keywords. If you ever need to build search, logs, or analytics at scale, Elastic will come up. ⥽ WebSockets > For anything real-time, chat, games, live dashboards, WebSockets are key. Your AI tools won’t design a fault-tolerant, low-latency messaging layer for you. Only learning, building, and breaking things will. ⥽ Distributed Tracing > In a microservices jungle, tracing lets you see exactly where requests slow down or fail. AI will tell you “add tracing,” but you have to know where and how to trace. ⥽ Logging & Monitoring > Nobody wants to get a “site down” call at midnight with zero logs. Learn what, when, and how to log. Monitoring will help you detect issues before users even see them. ⥽ Concurrency & Race Conditions > Even the best AI misses subtle concurrency bugs. Learn how to write and review multithreaded or async code. You’ll save days in debugging. ⥽ Load Balancers & Circuit Breakers > When your backend goes down or traffic spikes, these patterns keep your service up. Theory is easy, handling real-world outages is something you have to experience. ⥽ API Gateways & Rate Limiting > If you ever ship a public API, you’ll thank yourself for knowing this. Otherwise, get ready for abuse, cost overruns, or worse. ⥽ SQL vs NoSQL > Not every problem is a nail, not every DB is a hammer. Know when to pick each. ⥽ CAP Theorem & Consistency Models > Distributed systems fail in surprising ways. CAP and consistency are what separate coders from engineers. ⥽ CDN & Edge Computing > Speed isn’t just about code. If your users are global, your infra better be too. ⥽ Security Basics > Learn OAuth, JWTs, and encryption because you don’t want to be the engineer who lets a breach happen. ⥽ CI/CD & Git > Automated tests, clean deployments, and rollback plans. AI can’t fix a botched deploy for you. Write some scripts, push your local hardware, break things, and figure out why. This is how you build instincts that AI can’t teach. AI will make you faster, but these fundamentals make you effective and productive. 

If you want to land a $100k+ remote job offer as a software engineer in 2025, I would 100% suggest you invest your time in these technologies (based on my experience from the last 15+ years). 1/AI agents are the hottest thing right now - half of SF is building agent startups, why? Because they’re the closest thing to AI automation before AGI. - think of them as LLMs that make decisions, automate workflows, and interact with real-world apps (Gmail, WhatsApp, databases). - startups are racing to build voice agents, chatbot-based automation, and AI-driven assistants and they need engineers who know how to integrate LLMs with real-world APIs. - learn LangChain, OpenAI API, and automation frameworks to get into this space. 2/ Browser automation is the secret weapon for AI companies - Many AI companies need their models to control and interact with websites, booking flights, scraping data, handling forms. - Startups like Induced AI and Browserless are being built purely to automate browser interactions. - If you learn Selenium, Playwright, and Puppeteer, you can land jobs in AI companies that need large-scale browser automation for their systems. 3/ Vs code extensions and developer tools are printing money - AI-powered developer tools are booming, Cursor, Cody, and Devika are at billion-dollar valuations. - Understanding how VS Code works under the hood, how to build extensions, and how to index and parse large codebases efficiently is a valuable skill. - Want to future-proof your skills? Learn how to build AI-powered coding assistants or improve existing developer workflows. 4/DevOps and cybersecurity will never go out of demand - Every company hitting scale needs DevOps engineers to optimize cloud costs, monitor infrastructure, and automate CI/CD. - Good DevOps engineers are rare, and companies pay massive salaries for experts who can save them millions on AWS bills. - Cybersecurity is another evergreen skill. Even AI-written code will have security vulnerabilities. If you understand penetration testing, bug bounties, and infrastructure security, you will always be in demand. 5/ AI image and video generation will only grow -Companies like Runway, Ideogram, and Midjourney are disrupting design, media, and content generation. - Learning diffusion models, LLM-based video generation, and AI-powered media tools will put you in one of the fastest-growing industries. - This is a difficult field to break into, but if you can build AI-powered media tools, you’ll be ahead of 99% of developers. Pick a field, go deep, and build real things. AI is making engineers 10x more productive, which means companies are hiring fewer, but better engineers. Don’t just learn—show proof of work.

I’m a Software Engineer with over 7 years of experience, now building systems at AWS, and I still don’t call myself an expert. The longer you work in this field, the more you realise how much there is left to learn. If I were starting over in 2026, these are the 60+ concepts I'd double down on: ► Software Engineering Essentials 1. Learn one programming language deeply (Python, Java, Go, C#, Rust etc) 2. Git for version control 3. Writing clean and maintainable code 4. Build and dependency management tools 5. Testing fundamentals (unit, integration, mocking) 6. CI/CD basics 7. Basic cloud concepts (AWS, GCP or Azure) ► Core API Design 8. REST fundamentals (resources, status codes, verbs) 9. Resource modelling & naming conventions 10. Request/response schemas and validation 11. Error handling & structured error formats 12. API versioning 13. Authentication vs authorisation at the API layer 14. Rate limiting & throttling 15. Synchronous vs asynchronous APIs 16. gRPC basics (protobuf, when it's useful) ► Networking Fundamentals 17. OSI & TCP/IP models 18. IP addresses, ports and subnets (CIDR basics) 19. TCP vs UDP 20. DNS (what it does and how it fails) 21. NAT and basic firewall / security group concepts 22. L4 vs L7 load balancing ► Databases & Storage 23. SQL vs NoSQL 24. Data modelling (normalisation vs denormalisation) 25. Transactions (ACID) 26. Indexes and query plans 27. Replication 28. Partitioning / Sharding 29. Consistency models (strong, eventual) 30. Hot partitions & key design ► Web Servers & Protocols 31. HTTP methods, status codes, headers, and cookies 32. HTTP/1.1, HTTP/2, HTTP/3 (behaviour differences) 33. TLS / HTTPS basics 34. Request lifecycle in a web framework (parse → route → execute → respond) 35. Middlewares (auth, logging, validation) 36. Reverse proxies & gateways (NGINX, Envoy, API Gateway concepts) 37. L7 load balancing and routing 38. Routing & URL mapping 39. Connection handling & keep-alives ► Distributed Systems Fundamentals 40. Stateless vs stateful services 41. Horizontal scaling patterns 42. Idempotency in distributed flows 43. Distributed locks (and why they are often a smell) 44. Leader election and consensus basics (ZooKeeper/raft etc) 45. Failure detection 46. Strong vs evenatual consistency tradeoffs 47. Caching fundamentals (TTL, eviction, cache-aside, stale data) 48. Queue & stream fundamentals (producers, consumers, ordering, retries) ► Observability & Monitoring 49. Metrics, logs, traces 50. Dashboards & alerting 51. Health checks 52. Distributed tracing basics 53. Incident response / Oncall Remember, you don’t need to memorise any of this. But if you understand these fundamentals, you’ll be able to work on any system out there. Save this. Study it slowly. And stay consistent. #softwareengineering

We analyzed nearly 1,000 Software Engineering job listings to uncover the skills that will define the next year of hiring. The data reveals a clear shift: . 1. The Core Powerhouses JavaScript (437) remains the undisputed king of the web, while TypeScript (278) has solidified its position as the mandatory professional standard for enterprise-grade codebases. Meanwhile, Python (326) continues its dominance as the essential bridge into the data and AI domains. . 2. React still is the King, but the "Stack" is Growing While React (374) dominates the UI landscape, it is no longer enough to know the frontend in isolation. The "Modern Stack" has expanded vertically to include: AWS, Docker, AI integration and Machine Learning, Git, Agile methodologies, and CI/CD pipelines. . 3. The Rise of the "T-Shaped" Professional Employers are moving away from hyper-specialized "siloed" developers. They are hunting for T-Shaped professionals: experts who lead with a deep knowledge of a core stack (like React/JavaScript) but maintain a broad, working mastery of the entire development lifecycle. . 💡 The Bottom Line Specialization is your entry point, but versatility is your job security. The most successful candidates for 2026 are no longer just "writing code"—they are AI-enabled system architects capable of building, deploying, and scaling intelligent applications from end to end.

I’m working as a Software Engineer at Facebook (Meta) with over 20 years of experience. If I were beginning my career again in 2026 and wanted to become an ML Engineer, these are the skills I would master first… [1] Python as the main language - Write Python every day, focus on clean functions, classes and modules. - Automate boring tasks like data cleaning, file handling and API calls. - Pick one backend stack, for example FastAPI or Django, and build simple APIs for your models. [2] Core machine learning fundamentals - Learn supervised and unsupervised learning, loss functions and metrics like accuracy, F1 and AUC. - Implement key algorithms from scratch in Python, such as linear regression, logistic regression, decision trees and k-means. - Take real job descriptions and map each requirement to a concept or method you know. [3] Deep learning and frameworks - Pick PyTorch and learn tensors, modules, optimizers and training loops. - Build small projects in vision and text, for example image classification and sentiment analysis. - Recreate a few public projects end to end, from raw data to a trained model with clear metrics. [4] Software engineering and production thinking - Use git every day, write tests, add logging and handle errors in a clear way. - Design simple services that load a model once and serve fast predictions through an API. - Turn your experiments into repeatable pipelines with configs, scripts and a fixed folder structure. [5] ML lifecycle and MLOps - Track experiments and models with tools like MLflow, or with a clear manual system at the start. - Learn Docker and package a small ML service into a container you can run anywhere. - Schedule training and batch inference jobs with simple tools, then move to managed cloud services as you grow. [6] AI and LLM skills - Learn NLP basics, tokenization, embeddings and how to evaluate text models. - Use LLMs to build small features such as summarization, classification or simple chat flows. - Practice prompt design and learn at least one method like fine tuning or retrieval to adapt models to real tasks. [7] Communication and soft skills - Explain every project with three points: problem, approach, and impact. - Write short docs for your work and treat them as part of your portfolio. - Practice speaking your thinking during mock interviews so your reasoning stays clear while you code. [8] Cloud foundations - Pick AWS and learn core services like S3, EC2, and one database option. - Deploy at least one ML service to the cloud, even if it is simple. - Learn basic cost and reliability trade-offs so your designs stay lean and practical.

As a Lead Software Engineer at the United Parcel Service (UPS) in the USA, if I were hiring for my team, I would NOT interview for:  —  memorized syntax & obscure language trivia   —  theoretical edge cases you’d never actually see in production   —  framework-specific knowledge that becomes obsolete in a year  Instead, I would interview for:  —  logical reasoning & problem breakdown   —  understanding trade-offs in system design   —  debugging skills & real-world troubleshooting   —  ability to work with distributed systems & cloud architectures   —  adaptability to learn new tools & frameworks quickly  Especially when working with Cloud systems at scale, the ability to think critically, design for failure, and optimize for efficiency is what truly matters.  Because languages change.   Frameworks come and go.   But strong engineering fundamentals? Those last forever.

Python developer says Python is the future. JavaScript developer says nothing beats Node.js. Android developer says Kotlin is best. Java developer stands by Java. iOS developer says Swift is unbeatable. PHP developer says PHP runs the web. But here’s what most “Software Engineers” eventually learn: → Frameworks keep changing. → New languages pop up every year. → Trends shift. “Best practices” get rewritten. → What’s “in demand” today might be forgotten tomorrow. But… → Writing clean code, → Understanding data structures, → Knowing how computers manage memory, → Designing for clarity, performance, and maintainability These things never go out of style. A true engineer can switch stacks. Because you see the patterns behind the syntax. You understand the “why” not just the “how.” You’re not loyal to tech, you’re loyal to problem-solving. Master fundamentals, and the rest is just translation. Because languages fade, but great engineering lasts forever.

The Skill Flip for Software Engineers I just published the first in a new role-by-role series on what AI is actually doing to specific jobs and how to build on rising skills. First up: software engineers. Remember all the headlines a few years ago about the "end of programming" and social posts claiming bosses would fire 75–90% of their engineers thanks to AI, while the ones left would just be prompt monkeys? No surprise, but that didn't age well. The Financial Times ran the numbers across millions of job adverts. Developer job openings are actually up. From what I see AI is not removing skills from the stack. It is reordering them. What's receding? Coding, syntax memorisation, debugging. Still necessary, but no longer where the value lives. What's rising? Architecture, problem decomposition, agent management, code review, test design. Harder to learn but harder for AI to replicate, and that's where the gap between good engineers and average ones is opening up fast. What is happening to software engineers now is a preview of what is coming for everyone else. I give a full breakdown plus how to build each rising skill in the article. https://lnkd.in/eBr5q2Y8  #SoftwareEngineering #AISkills #TheSkillFlip #AI

𝗪𝗵𝗮𝘁 𝗠𝗮𝗸𝗲𝘀 𝗮 𝗚𝗿𝗲𝗮𝘁 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗯𝘆 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 In a research paper in 2019, a group of researchers from Microsoft and the University of Washington asked 59 experienced engineers what makes engineers and what makes great software engineers. 𝟭. 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗰𝗵𝗮𝗿𝗮𝗰𝘁𝗲𝗿𝗶𝘀𝘁𝗶𝗰𝘀 Personal qualities pertain to your own identity. According to interviewees, these cannot be acquired in the job. ➡️ Improving—Unsatisfied with the status quo, they are constantly looking to improve themselves, their product, and their surroundings. ➡️ Passionate — intrinsically interested in the area they are working in (i.e., not just in it for extrinsic rewards like a paycheck). ➡️ Open-minded — willing to judiciously let new information change how they think. ➡️ Data-driven — taking and evaluating their actions and software measurements, often relative to expectations. 𝟮. 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗺𝗮𝗸𝗶𝗻𝗴 Your decision-making style is based on your ability to integrate context, probability, and an awareness of how your choices will manifest in reality. Knowing things from books is insufficient. ➡️ Knowledgeable about people and the organization - Figuring out who can help you, what they know, and who can provide the necessary context are all essential skills. This is particularly important for larger companies. ➡️ Sees the forest and the trees—Considers circumstances at various abstraction levels, including technical specifics, market trends, the company's mission, and operational requirements. What effect does each have on the work you do? ➡️ Updates their mental models - Keeping up-to-date their mental models through evaluating changes in their context ➡️ Handles complexity - Able to grasp and reason about complex and intertwining ideas 𝟯. 𝗧𝗲𝗮𝗺𝗺𝗮𝘁𝗲𝘀 Software development is a team sport; thus, how you lead and collaborate with your team counts. ➡️ Creates shared context—Modifying your message to fit the other person's comprehension allows you to operate from the same starting point. ➡️ Creates shared success - Enabling success for everyone involved, possibly involving personal compromises. ➡️ Creates a haven—a safe space where engineers can learn and improve from mistakes and situations without negative consequences. ➡️ Honest - Be truthful and have integrity with your actions and words. 𝟰. 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 This part includes software engineering and how we craft code and solve problems. ➡️ Elegant - Simple and intuitive. ➡️ Creative - Original solutions based on knowledge of the situation, existing solutions, and their shortcomings. ➡️ Anticipate needs—Software that operates continuously and changes its configuration with little assistance is regarded as excellent. However, it cannot be made so future-proof that it significantly hinders current speed. #technology #softwareengineering #programming #techworldwithmilan #productivity

10 Underrated Skills Every Software Engineer Needs (And How to Master Them): Having lead hundreds of engineers, I've seen a set of skills jump to the top as the ones that are most critical to build. Hint: It isn't a specific technical skill. Master these and make them visible to stand out: 1. Know What Not to Build - Always ask: Is this solving the right problem? - Focus on what will move the needle. Look at the long-term cost. 2. Make Things Simple - Simplify code, systems, and ideas. - Break down problems into small pieces. 3. Communicate Ideas Clearly - Explain complex ideas with clarity. Keep it concise. - Write down your ideas. 4. Progress > Perfection - Ship, learn, and iterate. No system is perfect. - Focus on continuous improvement in everything you do. 5. Deeply Understand User Problems - Understand the user and the larger landscape. - Use this to guide priorities and decisions. 6. Ask the Right Questions - Use questions to challenge assumptions and reframe problems. - Dig deeper with Why? (repeat Why until you get to the root). 7. See Failure as Data - Treat mistakes as opportunities to learn and grow. - Review mistakes without curiosity, not defensiveness. 8. Prioritize Experimentation - Iterate to reduce risk and improve outcomes. - Don't wait for certainty, test ideas and move quickly. 9. Always Learn - Continuously learn. New tools, perspectives, and approaches. - Spend time learning about the business, too. 10. Focus on Results - Look beyond your part to how it connects to the whole. - Think end-to-end and focus on impact. What I have seen over and over again: These skills separate the standout engineers. Master these, and you’ll become a better engineer, a stronger teammate, and build products users love. What skill do you think is most important for engineers to master? Share your thoughts below. ♻️ Repost to help connections who are ready to grow. ➕ Follow me, Melody Olson, for more like this.