Choose your context
The useful question is not whether I know a particular tool. It is whether I can help with the problem in front of you. Start with the path that sounds most like your situation.
The thread through the work
A slow screen led to API design. An API bottleneck led to event-driven architecture. Product questions led to AI agent experiments. Watching teams work by hand led me toward RevOps automation. An interest in markets pulled me toward quantitative finance. I still enjoy the details, I have just learned to judge them by what they make possible.
VentureDive
Dunzo
Digital Dividend Global
Digital Dividend Global
Star Marketing Pvt. Ltd.
Divine Virtuality
Civar.com
Where I am most useful
Usually, something important has outgrown its first design. The team needs someone who can understand the existing system, make the trade-offs visible, and still ship.
01
Turning assistants, agent workflows, and retrieval systems into dependable product features—not demos that fall apart around real data or real users.
02
Finding the expensive path through serverless and event-driven systems, then improving throughput, reliability, and the ability to see what is actually happening.
03
Comfortable discussing experiments, methodology, modeling, and technical uncertainty with professors, labs, and academically minded teams.
04
Helping teammates raise code quality, architectural judgment, and development velocity without losing clarity.
Working Stack
A practical stack shaped by shipped projects, not by trend-chasing.
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TypeScript180K lines migrated off JavaScript, and everything since
Next.jsServer-rendered product and content surfaces
OpenAILLM-powered assistants and workflows
n8nAutomation wiring for GTM and RevOps work
TerraformInfrastructure as code
AthenaQuery tuning that took 300M+ event scans ~12x faster
CursorWhere the day-to-day implementation happens
Education
My education path blends core computer science, scholarship-backed advanced learning, and an increasing pull toward finance, research, and graduate study.
2019 - 2023
Bahria University, Karachi. Graduated Cum Laude with a 3.76 / 4.0 CGPA and a 70% merit scholarship.
2023 - 2024
The Coding School / Qubit by Qubit program supported by Google Quantum AI and IBM Quantum, taught with curriculum prepared by leading universities.
2024 - 2025
WorldQuant University. Applied machine learning for financial data — feature design, validation, and the failure modes specific to non-stationary series.
2024
McKinsey & Company. A selective leadership and problem-solving programme covering structured thinking, communication, and digital fundamentals.
2025 - 2026
WorldQuant University. Studying quantitative finance, econometrics, modeling, and AI-driven decision systems as part of a pathway toward deeper finance-oriented work.
Scholarship awarded
Received a 75% merit scholarship for an MBA in Leadership and Management at Valar Institute.
Operating Principles
I care about clean systems, but I care even more about whether those systems serve real goals without creating unnecessary complexity.
Architecture decisions should still make sense after the first sprint, the first new hire, and the first unexpected scale problem.
I would rather know a system well enough to improve it meaningfully than collect shallow familiarity for its own sake.
Strong engineering matters more when founders, researchers, hiring managers, and non-engineers can understand the trade-offs.
Collaboration Model
Whether the context is a company, professor, founder, or startup team, the delivery model stays grounded in clarity and momentum.
Define what success means, what constraints matter, and what type of collaboration actually fits.
Turn requirements into an execution path with reasonable scope, architecture, and trade-offs.
Keep communication clear, deliver incrementally, and reduce risk through steady implementation.
Document, hand over context, and make the result maintainable for the next phase of growth.
Held opinions
The best engineering work is ambitious enough to matter and disciplined enough to hold up when reality gets messy.
FAQ
The short version of how I think about opportunities and fit.
Next Steps
A research mindset doesn't cost you speed — it sharpens the assumptions under everything you ship faster. Here's how that actually plays out on a product team.
23% of roughly 3,000 remote workers picked loneliness as a top struggle in Buffer's 2023 survey — self-reported, not measured, but real. A separate study of 61,000 Microsoft employees found the more precise mechanism: cross-group collaboration fell 25% under remote work while scheduled meeting hours barely moved. The isolation is real. The fix most teams reach for, more scheduled calls, is aimed at the wrong half of it.
Both answers are already true, for different people. The payroll data shows a 19% employment gap opening for 22-to-25-year-olds in AI-exposed jobs while experienced workers show no gap at all. That split is the actual story, and it is not the one either side of the argument is telling.
Entry-level hiring at the tech majors is down 65% since 2019 and Stanford measures a 19% employment gap for 22-to-25-year-olds. But an LSE paper covering 243 million hires found that when you control for remote work, the AI effect largely vanishes. The cause matters, because the two have opposite fixes.
The leap from software engineering to a PhD in quantitative finance isn't traditional—but it's increasingly viable. Here's what the path actually looks like.
Working close to money forces habits most software teams can skip: real traceability, real correctness, real accountability for what breaks. Here's what carries over once you've built them.
The honest version is less alarming and more actionable than the headlines. WHO looked at the evidence in 2020 and declined to set a sitting threshold at all — but a million-person meta-analysis found something much more useful about what offsets it.
The finding that should worry you isn't that six hours of sleep degrades performance. It's that in the study which established it, subjective sleepiness stopped tracking objective decline — the impaired group did not know they were impaired.
