About
I build machine learning that has to hold when it meets the real world, not just a benchmark. The thread through all of it is one question: what holds under pressure, and what collapses when conditions shift.
I spent years in security research and built a cybersecurity company from the ground up. That instinct still drives everything I do: assume systems fail in strange ways, test the edge cases, and care more about what happens in production than what looks good in a demo.
Professional Journey
June 2025 - Present
New chapter. Moved beyond pure niche headhunting into full-spectrum talent acquisition, supporting hiring across engineering, operations, and business functions for one of the largest water and hygiene technology companies on the planet.
Part of the reason I wanted to work inside an organization at this scale was to understand the complex problems large companies deal with up close: the small operational frictions, talent patterns, communication gaps, and cultural decisions that are easy to miss individually but end up shaping how an organization actually works.
The interesting part: building Global Capability Centers in India from scratch. Org design, talent pipelines, cross-cultural integration, the kind of ambiguous, high-stakes problems where most playbooks break down.
February 2022 - May 2025 · 3 years 4 months
Three years in retained executive search at one of the world's top leadership advisory firms. Placed C-suite and VP-level leaders across aerospace, defense, IT, pharma, and life sciences for Fortune 500 clients. The work went far beyond sourcing: building market maps from scratch, understanding how leadership teams are structured, calibrating with boards and senior stakeholders, assessing executive fit, and approaching people who were not actively looking to move.
Executive search taught me how senior hiring really works when the stakes are high and the candidate pool is small. Most of the job is pattern recognition: knowing which signals matter, which narratives are noise, and how to evaluate whether someone can actually lead inside a specific business context. Built the kind of network you can only build by being useful to very senior people over a long period of time.
2020 - 2022 · 2 years
2016 - 2020 · 4 years
This is where I learned what building actually means. Co-founded a cybersecurity company to replace the legacy signature-based detection that every enterprise was stuck on. We built the real thing: cloud-native architecture, ML-driven anomaly detection, behavioral analysis engines, neural nets for threat classification, fully automated incident response pipelines. Raised $12.8M from Tier-1 VCs and angels who'd been executives at Microsoft, Amazon, and Google.
Scaled from a prototype in a room to a 15-person team closing mid-six-figure enterprise contracts. I set the hiring bar, built the eng culture, ran the GTM motion, learned to translate deep technical roadmaps into language that boards and enterprise buyers actually cared about. Exited through a strategic acquisition that validated the architecture and the bet. Four years of the hardest, most useful education I've ever had.
2015 - Present
Where it started. I taught myself security by taking real systems apart: reverse engineering binaries, writing exploit chains, and finding vulnerabilities in production software used at serious scale. Reported issues across Google, Apple, Meta, Microsoft, Amazon, and Uber under the handle "xedro", and I still help startups, open-source projects, and engineering teams find and fix the failure modes that matter. What stayed with me was not the thrill of breaking things, but the discipline behind it: tracing assumptions, finding edge cases, and understanding why systems fail when they meet reality. That way of thinking still sits underneath how I build models, debug infrastructure, and judge technical work.
My GitHub contribution graph reflects that I still ship code regularly, even as my work has shifted deeper into leadership and talent.
What I Work On
On the research side, the quieter obsession is making agentic models hold under real load, not on the benchmark that flatters them: sparse MoE pretraining, compound reasoning, calibrated doubt, and code as ground truth.
Glide is my take on how job hunting and hiring should feel, and it is the matching problem pointed at real people. A layout-aware extractor parses a résumé into a typed graph, a cross-encoder scores fit across a heterogeneous graph of candidates, jobs, skills, and companies, and skill claims get verified against actual work history. Same instinct as the lab, routed into people. No keyword roulette.
How I Think
I start with one question: what’s actually happening here? Not what the docs say, not what the demo shows. I ask it the same way whether I’m debugging a training run, evaluating a candidate, or designing an architecture. Break it into testable pieces, watch how the parts interact over time, trust data over narratives, and optimize for resilience, not elegance.
The most interesting problems sit at the edges, where foundation-model training meets agent execution, where security research meets autonomous verification, and where machine learning finally meets people.
”The task is not so much to see what no one has yet seen, but to think what nobody has yet thought about that which everybody sees.”
Arthur Schopenhauer