Engineering Career

"Service reliability math that every engineer should know" I think it's useful for engineers to understand what uptime and reliability mean in practice. These numbers paint a good picture of what's involved :) Now while service reliability is often reduced to a simple percentage, the reality is far more nuanced than those decimal points suggest. First, not all downtime is created equal. A single 8-hour outage has dramatically different business implications than 480 one-minute outages, even though both sum to the same annual downtime. This distinction is particularly relevant when considering service level agreements (SLAs) and how they’re measured. The impact of downtime also varies significantly based on when it occurs. Five minutes of downtime during peak business hours might cost more than an hour of downtime during off-hours. This temporal aspect of reliability is often overlooked in simple percentage calculations. Each additional nine of reliability typically requires an order of magnitude more engineering effort and operational complexity. Moving from 99.9% to 99.99% isn’t just a matter of being "10 times more reliable" – it often requires fundamental architectural changes: At 99.9% (8h 45m downtime/year), you might get away with single-region deployment and basic failover At 99.99% (52m 35s), you’re typically looking at multi-region deployment, sophisticated health checking, and automated failover At 99.999% (5m 15s), you need redundancy at every layer, real-time monitoring, and likely some form of active-active deployment At 99.9999% (31s), you’re dealing with advanced techniques like chaos engineering, automated canary deployments, and sophisticated traffic management While understanding the basic math of service reliability is crucial, the real engineering challenge lies in understanding the context, trade-offs, and business implications of reliability decisions. The next time you see a reliability requirement, don’t just think about the percentage – think about the entire socio-technical system required to achieve and maintain that level of service. The numbers are simple. The engineering reality behind them is anything but. #softwareengineering #programming

Every 10-20 years, a breakthrough technology promises non-developers to finally create software without needing to hire programmers. So far, every such technology resulted in the need for more devs… expert in this NEW technology (or knowing how to fix it up). Every. Time. Of course, there’s always a chance of *this time* being different. But the surest way to have more career stability + more options + be more in-demand so far in all cases has been become an expert in *the new thing*. Like integrating + working with LLMs. Here are 7 devs that already did and are now “AI engineers” after a few months (they are software engineers who can build whatever with LLMs:) https://lnkd.in/ea5ju-cs There seems to be little to no downside of doing this as an engineer and much upside. And there WILL be an explosion of demand in professionals to take over maintaining “vibe coded” software that works, generates money but would need improvements (eg cost less, add more features without breaking existing stuff; get audited for compliance, support enterprise features etc) Source of screenshot: Gary Bernhardt on X: https://lnkd.in/eEn5WH4z

🎭 𝐏𝐞𝐫𝐜𝐞𝐩𝐭𝐢𝐨𝐧 𝐯𝐬. 𝐑𝐞𝐚𝐥𝐢𝐭𝐲: 𝐈𝐓 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐬 Perception: “Just someone drawing boxes and lines on diagrams.” Reality: ✅ Master of Complexity – Designs solutions that balance business goals, tech constraints, and future needs ✅ Stakeholder Whisperer – Aligns executives, developers, vendors, and users (all with competing priorities) ✅ Requirements Detective – Captures functional and non-functional needs: security, scalability, compliance ✅ Legacy Wrangler – Modernizes decades-old systems while keeping the lights on ✅ Portfolio Curator – Oversees an ecosystem of apps for integration, cost control, and lifecycle health ✅ Change Leader – Prepares organizations for transformation without chaos ✅ Quality Custodian – Guards performance, security, cost, usability, and future maintainability ✅ Endless Learner – Keeps pace with AI, cloud, and regulation shifts ✅ Documentation Guardian – Ensures architectural decisions stay transparent In short: • It’s not just diagrams. • It’s shaping the digital backbone of an enterprise—through clarity, foresight, and relentless collaboration. 💡 Save this for when someone says, “So… you draw diagrams all day?” 😉 👇 What’s the most misunderstood part of YOUR role as an architect? --- ➕ Follow Kevin Donovan 🔔 ♻️ Repost | 💬 Comment | 👍 Like 🚀 Join 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐬’ 𝐇𝐮𝐛 – for resources, insights, and community. Subscribe 👉 https://lnkd.in/dgmQqfu2

You applied to 100+ jobs but no interviews? Here's what's actually happening. Your experience is valuable. You're just invisible. Let me explain why, and how to fix it. When you apply online, your resume goes into a database called an ATS (Applicant Tracking System). Think of it like a massive filing cabinet. Now here's the key: Some recruiters don't read every resume. They search. Just like you search Google, they search their database: "Python AND data analysis" "SAFe AND agile transformation" "Tableau AND dashboard" If your resume doesn't have their exact search terms, you’re making it harder to get discovered. You're not rejected. You're just not found. But here's the secret: The job description often tells you EXACTLY what keywords they'll search for. It's like having the answer key. Example from a real job posting: If they say "Experience with Snowflake required"... → They'll search "Snowflake" → Make sure you write "Built data warehouse in Snowflake…" Not "cloud database" or "modern data platform." Use their exact words: Snowflake. I've mapped out 80 keywords that get candidates noticed in 2025: Top searches happening right now: • Python, TensorFlow, LangChain (AI roles) • Kubernetes, Terraform, Docker (tech leadership) • Power BI, Tableau, SQL (data leadership) • SAFe, Agile, DevOps (transformation roles) Your action plan: 1. Read the job description carefully 2. Circle every tool, platform, or methodology mentioned 3. Add those EXACT terms to your resume (if you have that experience) 4. Use them naturally in your accomplishments Example: Instead of: "Led team through digital modernization" You say: "Led SAFe agile transformation using ServiceNow and Jira, reducing delivery time by 40%" You have the experience. Now make it searchable. Your next role isn't rejecting you. It just hasn't found you yet. You’ve got this! 💡 Save this cheat sheet of 80 searchable keywords ♻️ Share to help someone in your network Follow me for more insider recruiting insights

I constantly get recruiter reachouts from big tech companies and top AI startups- even when I’m not actively job hunting or listed as “Open to Work.” That’s because over the years, I’ve consciously put in the effort to build a clear and consistent presence on LinkedIn- one that reflects what I do, what I care about, and the kind of work I want to be known for. And the best part? It’s something anyone can do- with the right strategy and a bit of consistency. If you’re tired of applying to dozens of jobs with no reply, here are 5 powerful LinkedIn upgrades that will make recruiters come to you: 1. Quietly activate “Open to Work” Even if you’re not searching, turning this on boosts your visibility in recruiter filters. → Turn it on under your profile → “Open to” → “Finding a new job” → Choose “Recruiters only” visibility → Specify target titles and locations clearly (e.g., “Machine Learning Engineer – Computer Vision, Remote”) Why it works: Recruiters rely on this filter to find passive yet qualified candidates. 2. Treat your headline like SEO + your elevator pitch Your headline is key real estate- use it to clearly communicate role, expertise, and value. Weak example: “Software Developer at XYZ Company” → Generic and not searchable. Strong example: “ML Engineer | Computer Vision for Autonomous Systems | PyTorch, TensorRT Specialist” → Role: ML Engineer → Niche: computer vision in autonomous systems → Tools: PyTorch, TensorRT This structure reflects best practices from experts who recommend combining role, specialization, technical skills, and context to stand out. 3. Upgrade your visuals to build trust → Use a crisp headshot: natural light, simple background, friendly expression → Add a banner that reinforces your brand: you working, speaking, or a tagline with tools/logos Why it works: Clean visuals increase profile views and instantly project credibility. 4. Rewrite your “About” section as a human story Skip the bullet list, tell a narrative in three parts: → Intro: “I’m an ML engineer specializing in computer vision models for autonomous systems.” → Expertise: “I build end‑to‑end pipelines using PyTorch and TensorRT, optimizing real‑time inference for edge deployment.” → Motivation: “I’m passionate about enabling safer autonomy through efficient vision AI, let’s connect if you’re building in that space.” Why it works: Authentic storytelling creates memorability and emotional resonance . 5. Be the advocate for your work Make your profile act like a portfolio, not just a resume. → Under each role, add 2–4 bullet points with measurable outcomes and tools (e.g., “Reduced inference latency by 35% using INT8 quantization in TensorRT”) → In the Featured section, highlight demos, whitepapers, GitHub repos, or tech talks Give yourself five intentional profile upgrades this week. Then sit back and watch recruiters start reaching you, even in today’s competitive market.

For decades, career growth followed a familiar formula: More headcount. More budget. More scope.  That model is changing. In the AI era, careers won’t be built on span of control, they’ll be built on innovation density. Today, anyone - from ICs to execs - can scale their impact without more headcount, more budget, or more time. The playing field is flatter. The differentiator? How fast you can learn, apply, and compound innovation with AI. If you’re thinking about career growth, stop asking: “How can I get more?” Start asking: “How can I innovate more with AI?” The people who rise fast will: See problems through an AI-first lens. Move from manual to scalable. Iterate faster than the rest. Your team size won’t define your trajectory. Your creativity will. Your budget won’t signal your value. Your innovation density will.

No company can give you the most exciting, challenging, and impactful project around the year, so the ups and downs in the kind of work you'd get are expected. On average, you would get 4 months of high-impact work, 4 months of moderate work, and 4 months of mundane work in a year. Instead of feeling bad about it, leverage the time and energy to advance your career and secure more impactful projects in the future. Remember, the most important and impactful project is not given to the smartest engineer, but it is given to the one holding the track record of getting things done well and on time. The trust you instill by doing grunt work or maintenance would and should put you in a position to get the most impactful project. Given that the maintenance work does not eat up a lot of your mental bandwidth, use this breathing room to identify more significant problems within your organization. Spend time digging deeper into systemic issues, find a solution, create a project plan, and present it to the leadership. This showcases your initiative and capacity for impactful contributions. When I was given some mundane work, I used the additional time to figure out the root cause of recurring outages and proposed an architecture change to solve it once and for all. Because I was thorough with my homework, it became a no-brainer for leadership to approve. You can also use this time to enhance your visibility and influence within your organization. The easiest of all is to use this time to hold tech talks and mentor early engineers. These interactions will make it more likely that you'll be top of mind when exciting projects are conceived. To be honest, this is what I did at all of my stints across all the companies I have worked at. I gave talks on some of the lessons learned from past projects, new industry trends, or introductions to new technologies. This helped me establish a good reputation within the org. Remember, every phase cannot be exciting, but it is important to know how to leverage it well. Impactful projects will not be served to you on a silver platter, you need to earn it. ⚡ I keep writing and sharing my practical experience and learnings every day, so if you resonate then follow along. I keep it no fluff. youtube.com/c/ArpitBhayani #AsliEngineering #CareerGrowth

The most consequential decisions in a career are often the ones that look irrational in the moment. A common pattern in high-growth companies is that the most impactful roles are often the least defined at the outset. The title is unclear. The scope is fluid. By traditional metrics, it can look like a step down. That is the point. Early in your career, and often well into it, people optimize for position. They evaluate title, compensation, reporting lines. They try to map a linear path forward. This is a legacy framework from a more static economy. But in periods of technological acceleration, the variables that matter shift. High-growth companies compress time and push you beyond your prior experience. They push you to develop new skills quickly and operate beyond your prior experience. One year of work can feel like five. In those conditions, the job description becomes secondary. What matters is whether you are working on important problems alongside people who raise your standard. Careers tend to follow momentum. The environments you choose shape the trajectory more than the plans you start with. The challenge is that these opportunities rarely present themselves clearly. They look incomplete. Uneven. Risky. The question is whether you can recognize directional momentum early and commit before the outcome is fully defined. #schmidtsights

I'm convinced the Forward Deployed Engineer role will continue to grow. The hardest part of Enterprise AI isn't the model. It’s the "Head Knowledge" transfer. As a Forward Deployed Engineer, my job isn't just to write code; it’s to extract the implicit rules living in the heads of Subject Matter Experts (SMEs) and turn them into logic an agent can actually act upon. Here’s the reality: You can’t just quote "LLM benchmarks" to a guy running a 1000° aluminium smelter. Why? Because they know the stakes. You can't trust an LLM to decide the quality of an anode when a mistake means a catastrophic failure. They don't want to hear your "bullish" predictions; they want to see your proof. SMEs will call out your bullshit in seconds. The only way to win in this role is to build trust through consistency and hard data. You prove the work, you show the edge cases, and you respect the "head knowledge" that’s kept that factory running for 30 years. I’m bullish on the FDE role because we are the bridge. We don't just talk about AI but we make sure it survives in real factories. PS: If you're interested about this role and what tradecrafts make a good FDE, I'm thinking about collecting all this knowledge together. Of course, I'm still learning but this will come out soon.

YOUNG ENGINEER: WHY ARE YOU SPECIALIZING SO EARLY? I always find it fascinating when I sit down with fresh graduates in Civil Engineering. The conversation often goes something like this: “I’m interested in roads.” “I want to work in water.” “Structures are my passion.” And my next question is always: “So why did you choose Civil Engineering in the first place?” Don’t get me wrong—focus is important. But in a small economy like ours, where opportunities are already limited, narrowing your scope too soon can mean closing doors you haven’t even seen yet. Civil Engineering is vast, and boxing yourself into one corner at the start of your career can reduce your chances before you’ve even begun. My Own Journey When I graduated from university, I made a deliberate decision: I would learn everything I could, across the board. I started in roads, then moved into water and sanitation, then into structures. Over time, I mastered structural designs, water treatment plant designs, sewage lagoon designs, and pipeline designs. I became comfortable moving from one discipline to another, learning not just the theory, but the practical skills each field demanded. The Results of Staying Versatile Fast-forward 20 years, and I can confidently say it was the best professional decision I ever made. I have worked across multiple disciplines of engineering. Not once in 20 years have I been jobless. I’ve been part of over 200 projects, both small and massive in scale. When a road project comes—whether design or construction supervision—I work like a roads specialist. When a large water project lands, I take it on like a water expert. I’ve delivered irrigation projects, supervised bridges, contributed to hydropower designs—you name it. That flexibility has kept my career not only stable but exciting. It has also made me an asset to clients and employers who value professionals that can adapt to any engineering challenge. My Advice to Young Engineers In the early years of your career, don’t rush into a narrow specialization. Instead: Expose yourself to all areas of the profession—roads, water, structures, geotechnics, environmental engineering, and more. Learn by doing—seek diverse projects, even if they push you out of your comfort zone. Build a wide skill base—so that no matter the project, you can confidently say, “I can do that.” There will be plenty of time to specialize later, once you’ve built a solid foundation. But in the beginning, aim to be versatile. In a world where change is constant, versatility is not just an advantage—it’s survival. So, I ask again: Young Civil Engineer—why are you specializing so early? Joel Aita Chairman, Joadah Consult