Hashim Colombowala
Redwood City, California, United States
798 followers
500+ connections
About
Love building automation tools and services to solve problems.
Core…
Experience
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Education
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University of Southern California
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Masters Degree in Electrical Engineering with a focus on Digital Design, Computer Architecture and Computer Networks. Grader for Computer Architecture and Organisation class.
Academic Projects
• Operating Systems (C++) - Implemented Thread Synchronization, System Calls, Multiprogramming, Virtual memory and Distributed File Systems on a MIPs simulator with an incomplete OS.
• Directed Research (Verilog) - Created a Multi-cycle MIPS CPU on an FPGA board to simulate the 8088…Masters Degree in Electrical Engineering with a focus on Digital Design, Computer Architecture and Computer Networks. Grader for Computer Architecture and Organisation class.
Academic Projects
• Operating Systems (C++) - Implemented Thread Synchronization, System Calls, Multiprogramming, Virtual memory and Distributed File Systems on a MIPs simulator with an incomplete OS.
• Directed Research (Verilog) - Created a Multi-cycle MIPS CPU on an FPGA board to simulate the 8088 microprocessor.
• Digital System Design (VHDL) – Designed and Implemented an OOO execution processor based on the Tomasulo algorithm on a Spartan 3E-1200 FPGA board. -
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Licenses & Certifications
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Doron Katz
When I started out as a TPM, the must-have technical muscle was systems design. You couldn’t sit in a room with engineers and talk architecture without being able to map out load balancers, databases, queues, and failover strategies. That was the craft. Fast forward to today, and the conversations are shifting. I’m finding that understanding large language models—their tokenization, attention mechanisms, and inference trade-offs—is becoming the new systems design skill set. You don’t have to build the models, but you do need to understand how they work well enough to guide integration, cost forecasting, and reliability planning. Think about it: tokenization defines your cost model and latency profile. Attention mechanisms (and the limits around context windows) influence product feasibility and user experience. These aren’t abstract research concepts anymore; they’re design constraints in the same way replication lag or cache invalidation once were. So just like we used to sketch out sharded databases and traffic flows, TPMs today need to sketch out how prompts, embeddings, and model calls will behave under scale. It’s the same discipline of system-level thinking, just applied to a new substrate. The TPMs who lean into this will be the ones who stay credible and valuable in AI-driven roadmaps.
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Piush Sinha
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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