Balasaheb Bagul

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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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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Discussion over PRD is not a conflict, it’s collaboration. Healthy debates in product and engineering teams are a sign of passion, ownership, and care for building the right solution. Sometimes the voices get loud, sometimes perspectives clash but in the end, these discussions shape stronger products and better business outcomes. Let’s value these moments of constructive disagreement because innovation happens when diverse ideas come together. #Teamwork #Collaboration #Leadership #ProductManagement #EngineeringExcellence #Innovation #HealthyDebate

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Most of life is made up of projects. So why do we so often treat skill development like it will happen on its own, separately, after “the real work” is done? Projects are the work—and the opportunity. Read more about how to approach project-based skill development in my post on LeadDev: https://lnkd.in/eSXA_TGc

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Happy to share that our in-depth technical take on Disaggregated Serving - when it’s useful and how to orchestrate it for maximum benefit - is now public. Technical blog: https://lnkd.in/gwUGWGYE Deep dive https://lnkd.in/g3RymGQd [2506.05508v1] Beyond the Buzz: A Pragmatic Take on Inference Disaggregation #GenAI #LLM #Inference #DisaggregatedServing #SystemsOptimization #NVIDIA #AIInfrastructure

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Amazon has announced plans to invest more than $35 billion across all its businesses in India through 2030, building on nearly $40 billion invested in the country so far. This investment will focus on business expansion as well as three strategic pillars: AI-driven digitization, export growth, and job creation. #AmazonIndia #Amazon #MakeInIndia https://lnkd.in/ggC-ssYF

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GenAI, Transformers & Their Real-World Relevance Transformers, initially crafted for language translation, have silently evolved into the foundation of today's AI advancements. From decision making to content creation, their presence is ubiquitous. However, the true significance lies in the thoughtful application of this transformative power. GenAI has transcended beyond mere demonstrations and research. It is reshaping: - Customer experiences through personalized interactions - Operational strategies via intelligent automation - Knowledge tasks with tools that enhance human cognition scalability The crux lies in harnessing intelligence for tangible outcomes - amplifying intuition, streamlining decision-making, and hastening insights. I'm intrigued to learn: What recent GenAI application has resonated with you the most? Secondly, How many technologies do you know that ended up revolutionizing far beyond their original purpose? #GenAI #Transformers #AppliedAI #EngineeringLeadership #Innovation #ThinkBig #HVSays

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The best satisfaction and growth path for a leader is when you mentor your team and equip them to move to the next level. If you fear your successor is going to replace you, it will only lead to harbouring insecurity, mismanagement and eventual downfall of you and your team.

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