About
I work at the intersection of human decision-making and machine learning systems. My work spans three domains — technical recruiting, ML research, and building AI products.
I care about how complex systems behave under pressure, whether they are neural networks, executive teams, or global markets. I do not just observe these systems from the outside. I break them down, rebuild them, and pay attention to how they fail, adapt, and scale in the real world.
My technical foundation comes from hands-on work in offensive security research, startup building, and talent systems. That mix lets me move comfortably between model design, organizational dynamics, and the human choices that drive both.
Professional Journey
June 2025 - Present
Recently started exploring a new dimension of talent acquisition beyond pure executive search. Leading both technical and non-technical hiring initiatives across engineering, operations, and business functions for complex industrial and sustainability-focused technology solutions.
Building Global Capability Centers (GCCs) in India while solving strategic talent challenges that extend far beyond traditional headhunting: from organizational design and talent pipeline development to cross-cultural team integration and scaling challenges in emerging markets.
February 2022 - May 2025 · 3 years 4 months
Executive headhunter specializing in retained search mandates for Fortune 500 clients including TE Connectivity, Eurofins, OpenText, and Takeda. Focused exclusively on C-suite, VP-level, and senior director executive placements across aerospace, defense, IT, and pharmaceutical sectors.
Conducted deep market intelligence and executive talent mapping to identify and recruit passive senior leaders within highly specialized technical domains. Built extensive networks of executive contacts and maintained confidential relationships with top-tier leadership talent.
2020 - 2022 · 2 years
2016 - 2020 · 4 years
Co-founded a cybersecurity startup focused on replacing legacy enterprise security approaches with a cloud-native architecture. Led product, architecture, and early customer work, building threat detection systems that identified zero-day vulnerabilities before they were widely known and helped customers move from reactive to proactive security. Developed ML-driven anomaly detection and behavioral analysis engines for enterprise environments, including production-grade neural networks for threat classification and automated incident response.
Raised $12.8M across multiple rounds from experienced angels (including former Microsoft, Amazon, and Google executives) and Tier-1 venture investors. Learned to communicate a deep technical roadmap in clear business terms for boards and enterprise buyers while maintaining a high bar for technical and security standards.
Scaled the company from an early prototype to a 15-person team closing mid six-figure enterprise contracts, showing that a small, specialized team could deliver enterprise-grade security at scale. Built the initial hiring bar, engineering culture, and go-to-market motion, and ultimately led the company through a large strategic acquisition that validated both the technology and its fit in the market.
2015 - Present
Started as a red team operator, what began as curiosity about how systems break has evolved into a deep passion for information security that drives everything I do. Discovered vulnerabilities across major platforms including Google, Apple, Meta, Microsoft, Amazon, and Uber through systematic reverse engineering. Not for bounties, but because understanding security flaws at this level feeds my obsession with system resilience. Known in the security research community as "xedro".
This work has become essential to who I am as both a technologist and entrepreneur. The mindset of thinking like an attacker while building defenses shapes how I approach every technical challenge, from startup architecture decisions to evaluating security-conscious leadership talent. It's the foundation that connects all my other 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
I am building Glide, an AI-powered platform that matches technical talent with opportunities using deep learning models trained on hiring patterns, technical assessments, and career trajectories. It is where my recruiting experience and ML research meet in a single product.
My research focuses on making large language models more reliable and efficient in production, not just on benchmarks. I am especially interested in:
- Inverse scaling phenomena – understanding why some models get worse as they get bigger and what that reveals about intelligence
- Reasoning architectures – designing systems that can reason step by step without becoming too slow to use
- Deployment challenges – closing the gap between research ideas and real systems that teams can trust in production
I also advise companies on technical hiring strategy and executive search, helping them build teams and leadership benches that can actually deliver on ambitious technical roadmaps.
How I Think
I approach problems by first understanding the dynamics of the system in front of me. Whether I am evaluating a candidate, debugging a model, or designing an architecture, I start with a simple question — what forces are actually at work here.
In practice, this looks like:
- First principles thinking – breaking complex problems into parts that can be tested and understood
- Systems perspective – paying attention to how people, models, and structures interact over time
- Empirical validation – testing assumptions against real data instead of trusting elegant theories
- Long-term optimization – favoring resilient, adaptable solutions over short-lived wins
I do my best work on problems that sit at the edge of multiple domains, where technical depth meets human psychology, where research becomes product, and where individual decisions quietly shape organizational outcomes.
”No man ever steps in the same river twice, for it is not the same river and he is not the same man.”
— Heraclitus
I have learned that the most interesting work often appears in the overlaps that do not look obvious on a résumé. My path from security research to recruiting to ML was not linear, but each phase built skills that compound on the others. I try to follow the questions that keep my attention, especially when they sit at the boundary between people and systems, and let the categories catch up later.