Xiaonan Wang

The best advice I got on my promotions as a software engineer was from a Principal Architect at Amazon after he told me his story of getting promoted to Principal, and this has stayed with me my whole career. He said, very simply: > Your architecture can be a love letter to your technical brilliance, > but if it does not move customer and company goals, it will not move your level. That hurt a little. Because I recognised myself in it. A few years ago I wanted to design something with every shiny thing in the book: API Gateway, Lambda, DynamoDB, event bus, the works. On paper it was beautiful. In reality it was expensive, slow to ship, and did not match the skills of the team that would maintain it. So we shipped a hybrid architecture instead. Fewer moving parts, more reuse of existing services, cheaper to run, easier to hand over. Over time, here is how my thinking about promotions changed. 1. Start from the business goal, not the tech stack Before talking about Kafka vs SQS or MySQL vs DynamoDB, answer this first: What money, risk, or customer pain will this remove? Promotions track that, not the number of services in your diagram. 2. Ship finished products, not clever demos Leaders remember the feature that went live, stayed stable, and earned or saved real money. Drive the entire lifecycle yourself: design, build, launch, oncall, cleanup, docs. 3. Make the team faster, not just yourself Tools, scripts, templates, better CI, better dashboards. If people quietly say “things move faster when you are on this” your manager already has half a promotion case. 4. Do your share of the unsexy work Oncall, migrations, bugs, incident reviews. Senior folks who avoid this slow the entire org. Senior folks who lean into it become trusted very quickly. 5. Grow people, not just systems Mentor juniors, run design reviews, write clear docs, set standards. When your manager sees that you are already acting like the next level, formal promotion becomes a catch up, not a favour. 6. Be proactive about your promotion, not obsessed with it Have periodic one to ones. Talk about where you want to go and ask what evidence your manager would need to support that. Remember your manager has ten other people to manage. It is your responsibility to make your impact visible. Do not be desperate. Understand the timelines and work within them. As you get close to the boundary, collect the missing data points: impact metrics, incident write ups, design docs, peer feedback. Make the story easy to tell. Most importantly, work for the business, not for promotion-driven development. If your roadmap only exists to tick boxes on a promotion rubric, people can feel it. If your roadmap clearly creates value, saves cost, or reduces risk, promotion becomes the natural side effect. So instead of asking “What new tech can I use this year?” Ask, “Where can I create obvious business impact and leave the system and the people around me better than I found them?”

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Someone lost their job at Amazon when they were expecting their first child and looking forward to being a parent. Now their whole world is upside down. Someone got laid off in October, fought hard to find an internal role, and still got impacted in the next round. Someone who spent 10 years in the company, worked across three teams, and managed people with care, also lost their job today. It happens quickly. You get an email, the next thing you know you are locked out of the system, badge stopped working, chats gone. It feels like you were never part of the place to begin with. Layoffs are the worst part of this industry, and you can do almost nothing to prevent them once a list is made. A few things I want to say to anyone who got impacted today: 1. A spreadsheet decision is not a measure of your intelligence, your kindness, or the value you created for users and teammates. Please be kind to yourself. 2. Take time to process. Cry, vent, sleep. You are a human who just had their safety rug pulled, not a robot who must instantly “bounce back.” 3. The projects you shipped, the skills you built, the people you helped, all of that is real. A layoff cuts pay, not your track record. Do not feel like you’re not strong enough to find your way again, you are. 4. Tell friends, ex colleagues, mentors what happened. Ask for referrals, mock interviews, resume reviews, even just calls where you talk about anything except work. If you were not impacted, check on the ones who were. Nobody chooses to be on the receiving end of that email. The least we can do is remind them that the email is not the end of their story.

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At 3 AM on a Wednesday, Amazon needed me. I wasn’t even on that team anymore. Back when I was an SDE at Amazon, a Sev-2 had hit one of our services. Many customers were impacted. The team had been chasing it for hours. Still, they called me. I rolled out of bed, opened my laptop, and joined the bridge. The tricky part was that the fault started a week earlier. A code change had shipped behind a flag, traffic ramped slowly, and the failure signature was noisy. Logs were chatty in all the wrong places and silent where we needed them most.Dashboards showed errors, but no clear line to the root. We did unglamorous things. We wrote a minute-by-minute timeline. We diffed deployments, configs, and flags across regions. We checked queue backlogs and retry storms. We sampled logs with new filters. We bisected the traffic ramp. We rolled back the smallest safe thing first. Then we added one log, in one hot path, and the picture snapped into focus. By sunrise, we had the root cause and a fix. The impact was contained. Customers recovered. What stayed with me was not the fix. It was why they called. They did not call because I am smarter. They called because, over months, I had done the boring work. I wrote runbooks when no one asked. I paired with juniors on their first on-call. I shared context so others could make decisions without me. I showed up when it was not my ticket. Trust is not built in one night. It is built in all the small days that come before it. Ownership is simple. If it affects your customer, it is your problem. Org charts are for payroll, not for 3 AM. If you are early in your career, remember this: → Document the weird corners when you discover them. → Add one useful log where it hurts, not twenty where it is easy. → Keep a living checklist for rollback, not a static wiki page. → Teach one person the thing you just learned. → Read your dashboards when things are calm, not only when they are red. At 3 AM, you will not rise to the occasion. You will fall to the level of your habits. Build the right ones while the house is quiet. That’s how you earn trust that lasts beyond teams and ownership that outlives org charts. A few days later, this moment turned into an accolade from the team..  a reminder that trust is built long before it’s tested :)

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"If I get laid off tomorrow, I won't be able to find a job" is a fear many devs are feeling in this market, even though they have done solid work. I took my exit from Amazon after 5 years, even though it was the company I started my career with, and I went through the same strange transition period as I got out of Amazon’s ecosystem. Here is what I learned. At Big Tech, you attach your identity to a logo, which you shouldn’t do. You wake up one day and think, “I do not know software engineering, I only know Amazon engineering.” The fear feels real, but it is a story your brain is telling you, not the truth. A few realities that are easy to forget inside the bubble: 1. Tools change, problems repeat. Every company has its Brazil or Peru. Outside, the names are different, the core work is the same. People, trade-offs, prioritization. 2. Your skills are more transferable than you think. Distributed systems, debugging, on-call, writing design docs, working through half-broken requirements. These are valuable everywhere, not only in FAANG. 3. The market cares about more than LeetCode. Yes, you need to pass coding rounds. But people are getting rejected for zero soft skills as well. Clear communication, ownership, and calm under pressure are still hard currency. So what can you do if this fear is sitting in your chest today? Here is a simple plan. 1. Detach identity from employer Build something outside work. Sport, hobby, volunteering on the “corporate” side for a non-profit. You will see how much you know that has nothing to do with internal tools. 2. Rebuild confidence with small public projects Take the type of system you work on today and recreate a tiny version with public tools. Use AWS, Docker, GitHub, whatever fits. You are practising the same thinking with a different toolbox, and you can talk about this in interviews. 3. Create a boring preparation routine Do not stare at LeetCode and panic. Pick a small target. For example: one problem a day for 30 minutes, three days a week. Track progress. Accept that it feels horrible for the first two weeks. Everyone else is rusty at the start as well. 4. Talk to someone who has moved Mentor or ex-colleague. People who left after 4 to 10 years will give you a lot of perspective; it can be a very good thing. 5. Test the market before you are forced to Update your resume. Apply to a handful of roles. Take a couple of interviews purely as data points. Once you see interview loops again, the fear shrinks. You realise the bar outside is different from the story in your head. If you were smart enough to get hired, ship features, survive oncall and navigate that environment for years, you are absolutely capable of learning a new stack and doing good work somewhere else. Do not wait for a layoff to prove that to yourself. Start collecting wins outside the bubble now, so when change comes, you are moving from fear to a plan instead of from fear to free fall.

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You are in a system design interview at Amazon for an SDE-3 role and the interviewer has given you the question to Design Netflix. He then asks a follow up to you: How does Netflix knows when to show: Are you still watching? Is it just time-based? If it were time-based, you would see it every time. So what is actually going on under the hood? Both look like a simple pop-up. Underneath, it is a mix of product thinking, client logic, and backend events. Btw, if you’re preparing for system design/coding interviews, check out our mock interview tool. You can use it for free here: https://lnkd.in/gpCn7t2T [1] Start from the product goals The feature is not only about nagging people. - Save bandwidth and CDN cost if the viewer has slept or walked away - Avoid autoplaying potentially sensitive content in an empty room - Protect kids if parents start a show and leave the TV on - Do all this without annoying active binge watchers So the design must answer one question: "When is the user probably not here any more" [2] Naive design - pure timer Simplest idea: - If playback has been running for 2 hours, show Are you still watching - If user clicks "Yes", reset the timer Why this is weak: - Someone can binge 5 episodes in a row and gets interrupted in the middle of an intense scene - Someone can start a show, walk away after 5 minutes, and the platform will keep streaming for the next 2 hours - It ignores how many episodes were auto played, device type, time of day, user habits  [3] Realistic design - session and engagement signals Think in terms of a "watch session" and "engagement events". Signals the client can track: - Play, pause, seek, volume change - Episode finished and next episode auto started - Remote or keyboard input, UI navigation - Screen on or off events from the device - For mobiles: app background or locked state A common heuristic could be: - Count how many episodes have auto played without any user interaction - Track how long it has been since the last button press or navigation - Only trigger the prompt when both are high enough Example rule: - If 3 episodes in a row have auto played - And there has been no interaction for 45 minutes - Then, before starting episode 4, show Are you still watching This feels much smarter: - Active viewers who pause, skip intro, change volume keep resetting the "engagement clock" - Sleeping viewers do not touch anything, so the next episode is blocked by the prompt  [4] Client heavy vs server heavy design You can talk about two design choices. Client first: - All logic runs inside the app on TV, mobile, or web - Client keeps an in memory session model and decides when to show the popup - It still sends telemetry events to backend for analytics and future tuning Pros: - Works even with flaky network - Highly responsive, no extra server round trip Cons: - Logic must be implemented and updated across many platforms - Harder to quickly roll out rule changes Continued ↓

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I went from Senior PM to VP in less than 3 years. If I could do it all over again, here’s what I would change. I was a Sr. Director, and the Global Head of DEI for one of the world’s leading EDA companies, a top innovator in chip design. But eventually, I started to feel like I was hired to be the face of the function, and not the actual leader of the function. Reflecting on that experience as that company's first-ever DEI leader, there were natural growing pains I probably should have stuck around for. At the same time, another company came calling with a VP title, the highest base salary I’d ever seen, a 40% bonus, and a very nice stock package. It felt like a no-brainer. But here’s what happened: I left because I felt handcuffed in my Sr. Director role, and I let the money and title at the new company make the decision for me. What I didn’t do? The simplest thing. I didn’t check the company’s financial health. If I had just looked at their balance sheet or quarterly reports, I would have seen the red flags. Instead, I got caught up in the base pay and made a bonehead decision. The result? The 40% bonus came in the form of 10% and as bad as we were doing financially, I was thankful for that. The stock??? It dwindled down to nothing, eventually forcing the company to go private. And after I was there for a year and a half, the company laid off two-thirds of its workforce, and I was part of the fallout. The hardest part? If I had stayed at my previous company, I’d be a millionaire today just from the stock alone. Here’s what I learned: - Don’t just chase the salary, study the stability. - Culture and values outlast compensation packages. - Protect your long-term wealth, not just your short-term income. If you’re considering a new opportunity, don’t make the mistake I did. Look past the base pay and into the financial facts and future of the company! They say hindsight is 20/20, so I would love to hear from you. What’s one career decision you wish you could do over again and why?

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From Mid-Scale Hustle to Big-Scale Systems — My Leadership Journey When I worked at a mid-sized company — McAfee (formerly Intel Security), life as an engineering leader looked very different. I owned the entire tech stack end-to-end for the Real Protect SDK, which powered both enterprise and consumer security products. Decisions were quick. If I had an idea, we could experiment and roll it out within days. The visibility was immediate — the team saw the impact directly, and customers felt it instantly. But there were trade-offs: scale was limited, resources were stretched, and reliability sometimes gave way to speed. I wore multiple hats — tech lead, architect, and often the “on-call fireman.” Fast forward to today at Amazon. The canvas is massive — whether rolling out Prime membership features or building a multi-tenant portal to enable supply chain services for 1P & 3P sellers. Systems serve millions of users globally, reliability is non-negotiable, and playbooks ensure consistency across teams. I learn from some of the brightest minds, and every design decision forces me to think about scale, latency, and resilience. But here too, there are trade-offs: processes are heavier, decisions take longer, and ownership is often of just one slice of a much bigger puzzle. ⚡ What helped me move faster in this environment: Clarity of priorities → tie every discussion back to business + customer outcomes Cross-team relationships → build trust before you need alignment Document → Decide → Drive → cut endless meetings with written context & trade-offs Bias for small wins → ship incremental value instead of waiting for “big bang” launches Empowering the team → unblock quickly and let them own execution 💡 My takeaway: Mid-scale sharpened my ownership, versatility, and bias for action. Big-scale honed my systems thinking, discipline, and focus on reliability. The best leaders blend both — carrying startup agility into enterprise scale. 👉 If you’ve made the shift between mid and big companies, what was your biggest “surprise learning”? #EngineeringLeadership #TechLeadership #CareerGrowth #EngineeringManagement #ScalingTech #BigTech #StartUpCulture #LeadershipJourney

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Why is Redis so fast? There are many design decisions that make Redis blisteringly quick. We’ll focus on two that carry most of the weight. - First is Redis’s in-memory design + single-threaded event loop. Keeping data in RAM and processing commands in one core loop avoids locks and random I/O. - Second is its purpose-built data structures and lean wire protocol. Most commands hit O(1) or O(log N) paths with tiny per-op overhead. What happens when you read from Redis👇 Step 1: A client sends a command using RESP (a simple, compact text/binary protocol). Minimal parsing and small payloads reduce CPU and network overhead. No big JSON/XML blobs. Step 2: Redis’s event loop accepts the socket and queues work. Single-threaded command execution means no lock contention or context-switch thrash. If you want to use more CPUs you need to run more Redis instances in a cluster. Step 3: The command is parsed and routed via a hash table lookup to the target keyspace entry.  Lookups are O(1) on average thanks to efficient dictionaries and cache-friendly memory layouts. Step 4: The operation runs entirely in memory using specialized structures:   Strings, lists (quicklist), hashes (compact encodings), sets/intsets, sorted sets (skiplist + hash), streams, etc. They're are engineered for predictable, fast operations with tight CPU caches. Step 5: The response is written back through the same event loop.   Pipelining and batching can amortize syscalls and round trips, pushing throughput even higher. Step 6: Persistence and replication are off the critical path.   AOF uses append-only, sequential writes with configurable fsync; RDB snapshots happen in a child process.  Basically: we can lose data! This is a tradeoff Redis makes. Replication is async by default. The slow stuff is handled in the background so the hot path stays hot. Because Redis keeps data in RAM, executes commands in a single, lock-free event loop, and uses highly optimized data structures and a lean protocol, it avoids the latency traps of disk I/O, heavy parsing, and lock contention. That’s why it’s fast. Reed more about Redis here! https://lnkd.in/g2TEVzvx

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Why does an E3 level SWE at Meta make only ~190k/year, while an E8 level engineer makes over ~$2M/year, even though both engineers are ICs and spend the same time at work? I have spent the last 5 years at Meta as an IC. I joined with a little over 15 years of experience, and I’ve worked with many solid engineers in this time. Here is how I think about that compensation jump. 1. Same hours, completely different “unit of work” An E3’s unit of work is usually a task or a ticket. An E8’s unit of work is a multi year problem for the company. E3: “Implement this service, fix this bug, write this feature.” E8: “How do we cut infra cost by 20 percent across this product” or “How do we make this platform safe to scale to 10x users.” One person is paid to execute. The other is paid to decide what is worth executing in the first place. 2. Radius of impact E3 usually impacts a file, a service, maybe a small team. E8 shapes whole orgs and product lines. If an E3 ships something great, the impact is great but local. If an E8 ships the right platform, hundreds of engineers become faster and the company saves or earns millions every year. Comp tracks the area of the circle you influence. 3. Risk and downside protection At junior levels, mistakes are usually contained and reversible. At senior staff levels, a bad call can burn tens of millions or damage the brand. E8s are paid for judgment under ambiguity. They decide which bets the company should not make, which migrations can wait, which “shiny idea” is going to kill reliability. You pay more to people whose good judgment protects you from very expensive failures.  4. They scale themselves This can happen in a few ways. 1. Delegation with ownership They define the shape of the problem, then hand large pieces to other senior and mid level engineers while keeping the bar and direction clear. 2. Knowledge that travels They write RFCs, public comments, FAQs, wikis, internal posts. One answer helps hundreds of people who will face the same issue next quarter. 3. Tools over heroics Instead of unblocking people manually all day, they build tools, libraries or guardrails so others can unblock themselves. One well designed tool can save thousands of engineering hours every year. This is what “scaling yourself” actually looks like. The company pays for that multiplier.  5. Ownership of the “uncomfortable problems” Junior engineers usually work inside a well defined box. E8s take ownership between the boxes. They pick up problems that:  Span many teams and no one really “owns”  Require aligning leaders who disagree  Have product, infra, legal and security angles at the same time Most people avoid those because they are messy, political and slow. Very senior ICs lean into them. That is where a lot of value sits. Continued ↓

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Amazon Layoffs Incoming — And This Time, It's Principal Engineers Rumors[1] are circulating that Amazon is preparing another round of layoffs — and what's especially striking is that principal engineers are among those being targeted. This is a sharp reminder that: - Being senior doesn't make you immune to layoffs - As you climb the eng ladder (which is just another term for the corp ladder), you have to sustain your performance/impact, so cruising is not an option compared to common beliefs - Companies are entities that try to maximize revenue/profits – being laid off sucks, especially if you're on parental leave (or any kind of leave), on a precarious visa situation etc. I think we have seen enough layoffs in tech companies that these are probably the new normal. Companies who struggle to fire people through a regular process, will rely on layoffs to occasionally clean house. This sucks.

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An agent has no consistent definition. You can think of an agent as an autonomous or semi-autonomous system that can take actions on behalf of the user in a given environment, state, and make decisions or take actions that can accomplish certain tasks along a given time-frame, either by calling tools, dynamically selecting the next action to take or use a deterministic "workflow" (systems where LLMs and tools are orchestrated through predefined code paths). Using an Agent framework that gives you the ability to built systems in both, a reliable and a dynamic way can accelerate your agent development journey using agent abstractions. This means building Agentic systems that can reliably call tools, store memory (episodic/semantic/procedural), have comprehensive logging and observability, human in the loop workflows and the ability to build various multi-agent patterns flexibly based on your use case. A successful Agentic system in production is usually a combination of both, dynamic and predictable/reliable multi-agent systems. Strands Agents SDK gives you exactly these capabilities by treating each “agent” as a combination of a foundation model plus a suite of tools. You define a prompt and register your tools (decorated functions) in code, then Strands handles reasoning→planning→tool-execution cycles, local testing, and cloud deployment (ECS, Fargate, Lambda, EC2), along with support for all other agent abstractions provided above. Excited to share a hedge-fund analyst multi agent system: This uses the newest Anthropic's Claude 4 Sonnet/Opus that powers the Lead Analyst Agent, routing incoming queries to specialized sub-agents for fundamental, technical, and market analyses. Each specialist is wrapped as a callable tool (using the “agents-as-tools” multi-agent pattern), so the orchestrator never has to implement domain logic itself and can handoff the task to an agent as a tool. For sensitive operations (insider lookups), we utilize a HITL approval step that halts execution until a human grants consent. We also use meta-tooling that enables the Lead Analyst to generate, load, and invoke new custom tools at runtime—whether it’s a portfolio beta calculator or a pricer—without redeployment. Strands also embeds observability (Langfuse) and OpenTelemetry tracing so you can trace reasoning events, tool invocations, errors, and end-to-end workflows in real time. View more information on the code implementation here: https://lnkd.in/gJmwVyGi Code implementation: https://lnkd.in/gzTtJvJq Thanks to 🏄‍♂️ Cagatay Cali for being a reviewer/collaborator on this! Feel free to try it out and reach out with any questions/ideas. #aws #agenticAI #strands #agents #generativeAI

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As a Sr Principal Technologist and Bar Raiser at Amazon who's conducted hundreds of interviews, I've seen how difficult getting quality interview practice can be. Now, AI tools are changing the game. ## Amazon Interviews: Hard Yet Predictable Amazon interviews present a mind-bogglingly high bar: you must demonstrate you're better than 50% of current employees at your level. However, the structure follows a consistent pattern centered on Leadership Principles (LPs). https://lnkd.in/e7Dd8PHG Each interviewer typically explores 3 LPs in an hour, probing deeply into your experience—what you did, why you chose that approach, challenges faced, and lessons learned. For technical roles, expect additional focus on domain expertise, coding skills, and fundamentals relevant to your specialty. ## Using AI as Your Interview Coach Here's my recommended approach: 1. **Gather your materials as text files:**   - The specific job description   - Your resume   - Amazon's Leadership Principles 2. **Create a practice environment:**   Upload these text files to an AI assistant (Claude, ChatGPT, etc.) with a prompt like this (the part about "one by one" is crucial):   *"Act as an interview coach specializing in Amazon interviews. Read my resume, job description, and Amazon's Leadership Principles. Ask me behavioral questions one by one, probing into my experience and each LP. Include follow-up questions focusing on how/why I did things, challenges, and learnings. Conclude with STAR method assessment and improvement suggestions."* 3. **Practice strategically:**   - Focus on telling concise stories with meaningful metrics   - Get comfortable with the depth of follow-up questions   - Use AI feedback to refine your examples and delivery   - Utilize voice interfaces available in some AI tools to practice speaking about your experiences out loud—this builds verbal fluency crucial for the actual interview ## Why This Works What makes this approach effective is the unlimited practice and structured feedback without the cost of a coach. The AI won't get tired of asking you to elaborate or challenge your thinking—exactly what Amazon interviewers do. Also, every interaction with those tools is unique and it won't get repetitive. By simulating the intense questioning style and receiving feedback through the STAR framework (Situation, Task, Action, Results), you'll develop the muscle memory needed to navigate the real interview confidently. Remember to use these tools not just for rehearsing answers, but for genuinely reflecting on your experiences through the lens of Amazon's culture. The best candidates demonstrate authentic alignment with Leadership Principles, not memorized responses. Have you tried AI for interview prep? I'd love to hear your experiences in the comments.

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Micron, SK Hynix, and Samsung stocks are soaring but few understand why.   1. The structural reason:   - Memory, in particular High-Bandwith Memory (HBM) has become crucial to run LLMs for billions of users - Running LLMs is mostly is memory-bandwidth bound, not compute-bound - During decode, GPUs spend more time fetching weights and KV cache than doing math, making HBM the primary bottleneck   2. The supply chain reason: - As demand soared, the major players shifted production capacity to high-margin HBM - That led to undersupply of other memory types (SRAM, DRAM) which are still needed for AI   What this means for…   1. Business leaders Memory cost will drive up GPU pricing Even if you don't buy chips, AI infra costs will likely rise as supply chain players will pass on costs   2. Entrepreneurs After decades of silence, there is massive opportunity in innovating memory Its still overlooked by many but we will soon see more high valuation memory startups which will become attractive acquisition targets for the 3 big incumbents   📷: FT   …   Did you find this helpful? ♻️ Repost this to inform your network 🔔 Follow me for more AI insights 🔖  Subscribe to my newsletter

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Have you ever felt a mix of excitement and worry about how AI will change your job? I have! As a Senior Manager in Quality Engineering at Amazon, I have been asked this question multiple times by many professionals from various companies and different tech roles. I see firsthand how AI is reshaping not just testing, but every aspect of how we build and deliver technology. In my journey at Amazon, I have witnessed waves of technological change, each reshaping our roles as QA professionals. The arrival of AI isn’t just another wave—it’s a tsunami. I want to share a candid reflection today—not as an AI expert (I am not!) but as someone who’s spent years advocating for quality in the midst of evolving tech. The AI Shift: From Automator to Partner AI isn’t just another tool—it’s becoming a partner in how we design, build, and test. From AI-powered code analysis to dynamic test generation, we’re no longer just checking for bugs. We are driving the future of trust and innovation. Our Role: From Checkers to Quality Champions QA professionals—and frankly, anyone working with AI—will evolve from traditional roles to: • Curators of Data: Data quality fuels AI. We ensure it’s clean, relevant, and diverse. • Evaluators of Fairness and Ethics: Bias in AI has real consequences. Testing for fairness isn’t optional—it’s essential. • Guardians of Trust: AI decisions must be transparent. We ensure that AI systems earn and keep user trust. How to Lean In- Here’s how I am adapting—and how you can too: ✅ Understand AI basics, even if you’re not coding it. ✅ Involve yourself early—AI testing can’t be an afterthought. ✅ Experiment—play with new AI testing tools. ✅ Stay curious—quality isn’t just code correctness; it’s about user experience and trust. My Takeaway: Quality is More Than Code AI doesn’t change our core mission—it amplifies it. As QA professionals, as engineers, as leaders, we have a front-row seat to guide responsible AI. Let’s not fear this wave—let’s ride it! #AIinQA #ContinuousLearning #Leadership #QualityEngineering #FutureofWork #Amazon #QualityAssurance

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