Arnav Jalui

At DoorDash, we recently faced a challenge with API performance bottlenecks that were impacting response times and overall user experience. The Problem: Frequent API calls were creating latency issues, causing delays in critical operations. The Solution I Implemented: Leveraged Postman’s monitoring and alerting system to proactively detect bottlenecks. Optimized Java Spring Boot microservices by fine-tuning queries and integrating caching for frequently accessed data. Adopted Amazon DynamoDB for high-performance NoSQL data access, significantly reducing read/write latency. The Result: 40% reduction in downtime 25% faster page load times Improved customer satisfaction and reliability of our platform This experience reinforced the importance of combining proactive monitoring, cloud-native solutions, and scalable architecture to deliver real business impact. Performance optimization is not a one-time activity—it’s an ongoing mindset. #SoftwareEngineering #FullStackDeveloper #BackendDevelopment #FrontendDevelopment #Microservices #SystemDesign #WebDevelopment #Programming #CodingLife #SoftwareDesign #CloudComputing #AWS #AmazonWebServices #DevOps #CloudArchitecture #CloudSolutions #Serverless #Kubernetes #Docker #InfrastructureAsCode #Java #SpringBoot #SpringFramework #SpringCloud #SpringSecurity #JavaDeveloper #APIDevelopment #RESTAPI #GraphQL #ReactiveProgramming #PerformanceEngineering #ApplicationPerformance #SystemPerformance #SiteReliabilityEngineering #SRE #Monitoring #Observability #Scalability #HighAvailability #LatencyOptimization #CICD #ContinuousIntegration #ContinuousDelivery #Automation #PipelineEngineering #TestingAutomation #Jenkins #GitLabCI #AgileDevelopment #SoftwareTesting #Databases #SQL #PostgreSQL #MySQL #MongoDB #DynamoDB #DatabaseOptimization #DataEngineering #DataDriven #BigData #TechLeadership #Innovation #TechnologyTrends #EngineeringCulture #CareerGrowth #LinkedInTech #TechCommunity #FutureOfWork #DigitalTransformation #ITCommunity #JobOpportunity #SoftwareDeveloper #Fulltime #W2 #C2C #Contract #ContractJobs.

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💡 Spark jobs shouldn’t be black boxes. At InMobi, I built a custom SparkListener that emits structured, job-level telemetry, giving us precise cost and performance insights without overloading blobs. 🔧 Optimized for scale: • 200k+ jobs/month • Smart aggregation (p95/p99 with T-Digest) • Exported as structured logs (yes, not Prometheus) This approach gives us deep, programmatic insights into Spark jobs across Databricks, Kubernetes, and on-prem environments, be it Ad-hoc tasks or ETL pipelines. If you’re trying to move beyond monitoring into true operational intelligence, this journey might be worth reading. I want to thank Amit Khanna and Srikanth Sundarrajan for their continuous guidance and motivation, along with Archana H, for co-authoring the blog. https://lnkd.in/g9f9J8Sc https://lnkd.in/g9f9J8Sc #ApacheSpark #BigData #Observability #DataEngineering #CostOptimization #Kubernetes #Databricks #Telemetry #InMobi #LinkedInTech #EngineeringLeadership #PlatformEngineering

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RELEX Solutions AI-driven solutions are helping DoorDash streamline inventory management and optimize fresh product lifecycles across its DashMart network. It’s all about reducing waste, improving accuracy, and ensuring faster, more reliable deliveries to consumers.  Learn more: https://relex.link/47ir1I7  #AISupplyChain #RetailTech #InventoryOptimization #Sustainability  

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LiteLLM is an open-source proxy that connects your app to LLMs through one unified interface. Paired with Redis, it gives AI and ML teams a simple yet powerful way to unify access to LLMs, accelerate response times, and make AI apps real-time. Redis handles performance, memory, and data coordination that modern AI apps demand, while LiteLLM handles abstraction and routing. Our AI Product Marketing Manager Rini Vasan dives into: ▶️ What LiteLLM is ▶️ Why it works well with Redis ▶️ How LiteLLM and Redis work together Learn how to scale your LLM gateway with LiteLLM & Redis: https://lnkd.in/gqsTebF7

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3 Years at Amazon — 6 Things I’ve Learned So Far Three years ago, I walked into Amazon as a fresh grad — nervous, excited, and not entirely sure if I was ready for what was coming. Looking back now, it’s been nothing short of transformative. Here are a few things I’ve learned along the way 👇 ————————————————————— 🚀 1. Ownership Changes You My first project wasn’t easy. Things broke, deadlines slipped, and I doubted myself more than once. But Amazon’s culture of ownership taught me that you don’t wait for someone to fix it — you figure it out. That mindset has shaped not just how I code, but how I think. —————————— 💡 2. Diving Deep Is Where You Grow I’ve spent nights tracing logs, debugging chain reactions across systems I barely understood at first. Every “why” led to a “wow.” The more I dug in, the more I realized how much there is to learn — and that curiosity is your biggest superpower here. —————————— 👥 3. You Don’t Build Alone Some of my best ideas came from whiteboard sessions, random hallway chats, and even code reviews that turned into hour-long debates. Working with brilliant engineers taught me that collaboration isn’t just about dividing work — it’s about multiplying impact. —————————— 📦 4. Customer Obsession Changes Perspective At some point, you stop thinking about “features” and start thinking about “experiences.” Every decision — big or small — ultimately circles back to one question: Will this make the customer’s life easier? That shift in mindset is priceless. —————————— 💬 5. Communication Builds Trust Early on, I thought great engineers were defined by perfect code. Now I know — great engineers are defined by how clearly they think and communicate. Writing better docs, presenting ideas with clarity, and asking better questions have all made me a better teammate. —————————— 🧠 6. Growth Never Slows Down Three years in, I still feel like I’m just scratching the surface. From building scalable systems to exploring Gen AI, every day brings something new to learn — and that’s what keeps me excited to log in every morning. ————————————————————— It’s been three years of mistakes, breakthroughs, and memories I’ll always be grateful for. Here’s to growing, learning, and building what’s next 💛 What’s one lesson that’s defined your career so far? #Amazon #AmazonSDE #SDE #LifeAtAmazon #CareerGrowth #SoftwareEngineering #LearningEveryday

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Is the "Search & Browse" era of E-commerce officially evolving into something entirely new? 🛒🤖 I just finished watching an incredible deep dive with Manav Garg (Together Fund) on Think School. As a developer in e-commerce space, one takeaway really stood out: The transition to Agentic Commerce. For years, the industry has been obsessed with perfecting filters and recommendation engines. But the next big leap isn't a better UI, it’s the "headless" interaction between AI agents. (Timestamp: 18:05) The Shift: From UI to Infrastructure In the near future, customers may not spend 20 minutes scrolling. Instead, personal AI agents will negotiate with brand agents to find the best deal based on a user's specific goals and budget. (Timestamp: 15:08) What this means for the future of our craft: Logistics is the new Front-end: If the "Browse" layer becomes automated, the real competitive edge moves to the physical layer i.e supply chain, speed, and fulfillment infrastructure. This is where vertically integrated platforms really shine. (Timestamp: 21:31) The Rise of "Orchestration": With tools like Cursor and AI-assisted coding, we're moving away from boilerplate and toward building complex "Orchestration Layers" that allow these agents to talk to our platforms seamlessly. (Timestamp: 08:30) Efficiency as a Feature: As we integrate LLMs, the focus moves to making them profitable through smarter token usage and caching, turning "hype" into sustainable margins. (Timestamp: 36:52) We often focus on the 95% of time users spend browsing. (Timestamp: 17:52) But as that time shrinks, the platforms that win will be the ones built for agents, not just humans. Are we ready for Agentic Commerce, or are we still building for a world that's quickly moving past the search bar? Video Source: https://lnkd.in/ghfmbqw3 #Ecommerce #SoftwareDevelopment #AI #AgenticCommerce #ThinkSchool #Innovation

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10 Lessons from My 10 Years at Amazon — Part 1 Over my tenure at Amazon, I've learned some lessons that shaped how I think about building products and working as an engineer. 1. Customer needs outweigh our own ideas. I joined Amazon with a personal mission: figure out why many of my past projects failed. The answer was simple but uncomfortable. I was building things that I thought were interesting instead of solving real problems for customers. When the customer is not at the center, even technically impressive work can miss the mark. 2. Test automation is critical for long-term success. Before Amazon, I wrote code that worked but was difficult to maintain. I only truly learned how to write proper automated tests after joining. Tests are not just about catching bugs. They give teams the confidence to move fast without constantly worrying about breaking something. 3. Writing code is the easy part. This is even more true today with the help of AI. The harder work happens before any code is written: aligning people on the problem, defining requirements, designing the architecture, and planning execution. Strong preparation makes the implementation phase much smoother. 4. Strong opinions should be loosely held. Some of the strongest opinions I have seen come from Principal Engineers and senior leaders. What stands out is their willingness to change their minds when new data appears. Good decisions come from evidence, not ego. 5. Speed matters more than perfection. Waiting for the perfect solution often means moving too slowly. Progress creates learning. Teams that move quickly, observe results, and iterate usually outperform teams that spend too long trying to design the perfect plan. I will share five more lessons tomorrow. --- About me: Hi I’m Charles, a Software Engineer at Amazon. I also created PrepRoom.io to help job seekers build confidence through structured practice. In these uncertain times, I hope to support those facing layoffs or career challenges. Follow me, and let's navigate this together! ✨ FREE resume review on PrepRoom.io — 1,000+ resumes reviewed 🎤 Mock interviews on PrepRoom.io — hundreds mock sessions completed #SoftwareEngineering #TechCareers #CareerGrowth #Leadership #Amazon

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Over the past few months, our team at Amazon has tackled a tough challenge: how to help users discover the right products—quickly and intuitively?  That challenge gave rise to More Like This, a feature that helps customers explore similar items with greater accuracy and speed.  What started as a siloed technical project evolved into a lesson in how thoughtful infrastructure changes can transform the user experience. By connecting the work into Amazon's main search infrastructure, we aligned better with user intent while improving scale and performance.  The results: faster load times, more intuitive browsing, and meaningful lifts in engagement.  What struck me most was how behind-the-scenes improvements—the kind users never see directly—can have such a profound impact on satisfaction and discovery.  The lesson? Great product experiences are built on thoughtful technical decisions, experimentation, and a deep understanding of user behavior. Sometimes the most transformative changes happen in the code users never see, but always feel.  📖 Read more about More Like This, and other new shopping experiences here:   https://lnkd.in/gkY6Zd9G  #ProductDevelopment #UserExperience #TechInnovation #DataDriven

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Ever needed to run a regex search on your dataset—only to watch your queries crawl while your users tap their feet? You collect tons of log lines, events, or messages in your favorite analytics database and want to surface exceptions, patterns, or keywords with a quick REGEXP search. But as your data grows, those once-snappy queries start to lag... and lag... and lag. So why does this happen?   A basic regex search forces your database to inspect every value— O(n) time for n records. If you’ve got 100 million rows, even a blazing-fast system starts sweating. Now you start thinking: Can I make this faster with indexing? What options are there? - Start simple: Use a dictionary scan. If your text field has low cardinality—a few repeated values—Pinot’s dictionary scan is O(d), much better than O(n). But if that dictionary is huge? Performance still drags. - Get smarter: Enter Lucene's FST (Finite State Transducer) Index. It builds an automaton (think: compressed trie) that encodes all possible field values as a searchable graph. Now, rather than scanning the whole dataset, It matches your regex just once across the FST— O(k) time for your regex pattern length! Suddenly, 30-second queries finish in milliseconds, no matter the table size. - Still want more? Add inverted index alongside FST. For complex queries—mixing regex and filters—you get lightning-fast bitmap matching plus rapid regex search. Regex is fast, filters are fast, and everything stays real-time. But is indexing worth it? You might worry about extra storage or write overhead. What if you get all of these. Here comes Pinot’s FST(link in comment) and inverted indexes are so efficient you can build them inline, as new data lands in real-time. A tiny storage premium for massive query speedups—an unbeatable trade-off, especially as your workloads scale. Have you hit the same regex wall? What tricks helped you push past it? If you’re in Bangalore and want to geek out on real-time indexing and analytics internals—you would want to be at Meesho tech (https://lnkd.in/gDM2t8EZ) meetup. That’s where the latest Pinot tips and deep dives are brewing! StarTree Meesho Gnanaguru SattanathanJayesh Asrani #DataEngineering #IndexingMatters #BigData #PerformanceOptimization #ApachePinot #MeeshoTech #RealTimeAnalytics

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