Hi
I am Nikhil
An engineering leader
An unrepentant believer in calm engineering
The work
is about building engineering organizations that ship calmly — even when the business is moving fast
Twenty plus years across banking, global professional services, mobile and web at scale, an AI-powered product platform, and now an AI-first SaaS engineering organization. One throughline: predictable engineering teams beat heroic ones, every single time.
By day, I lead engineering organizations through the messy, exhilarating work of scaling. The shift from 15 to 60+ engineers. The migration from heroics to systems. The transition from "we'll figure it out" to delivery governance, AI-assisted workflows, platform reliability, and an engineering operating model that holds even when the org doubles in two quarters.
What follows is the story — how I got here, the leverage I've come to trust, and what I'm building right now. I'm an operator, not a founder; I've actually run the on-call rotation, sat in the war room at 3 a.m., and signed off on the runbook the next morning. The work below is the version of that story I'd tell over coffee.
Three chapters. One thread.
From global rollouts to AI-native execution.
It started with pipelines, banking systems, and a £5M rollout that absolutely could not fail.
I cut my teeth in enterprise IT — operations and software engineering across IBM and NIIT SmartServe, running enterprise banking and financial services delivery with the kind of compliance discipline that bakes itself into you. 98%+ compliance outcomes. Distributed teams. The kind of SLAs you don't miss twice.
Then came Clifford Chance in London. I ran a global document management migration across 10 international offices with zero operational downtime, managed enterprise technology programs up to £5M, and held a 100% audit and governance compliance record across one of the most regulated environments in professional services.
The lesson from that decade: boring engineering is the most expensive thing in the world to build — and the cheapest thing to operate.
Then came the wild years — operations, multi-region delivery, and an AI-powered product platform before "AI" was a buzzword.
As COO at Lall & Sethi, I led the operational transformation of a 50-person professional services org — 18% cost reduction, 20% retention lift, 25% annual revenue growth without proportional headcount.
From there to Dubai, running 15+ concurrent programs at Apptology across teams in Dubai, India, and Eastern Europe — budgets from $200K to $2M+, twelve major digital transformations delivered on time, and 95%+ client satisfaction across retail, healthcare, and logistics.
Then Builder.ai's Studio Store — global delivery leadership across the US, UK, and APAC. A portfolio of 40+ concurrent enterprise engagements, AI-powered product experiences, 85%+ renewal rates, and recovery work for accounts everyone else had given up on.
And now I'm building the engineering organization I'd always wished I had.
I joined Sequifi to lead product engineering, platform engineering, and DevOps/SRE through an AI-first transformation. Today I'm Director of Product Engineering — accountable for a 60+ engineer organization across 6 teams, a layer of 5 EMs and Tech Leads, the engineering budget, the hiring plan, and the platform reliability posture supporting $50M+ in ARR.
The systems work has shown up in the numbers: 40% reduction in production incidents, 35–40% improvement in on-time delivery, sustained 99.9%+ uptime, and roughly 25% velocity gain from AI-assisted workflows — without compromising review discipline, architectural oversight, or the human-in-the-loop quality bar.
I'm a member of the executive leadership team, partnered directly with the CEO, CTO, and CPO on strategy, organizational design, and board-facing engineering communication. The work is not done.
Selected impact
The numbers that earned the right to be on this page.
Pulled from the most recent organizational scaling work at Sequifi, with prior chapters spanning Builder.ai, Apptology, Clifford Chance, and earlier roles.
The pillars
Three things I keep building, in every engineering organization I touch.
Calm engineering organizations, built deliberately.
Organizational design, delivery governance, hiring loops, and a leadership bench that scales with the company. Engineering managers who feel supported, tech leads who own outcomes, and a leadership-of-leaders layer that doesn't break at the seams when the org doubles.
AI as engineering leverage, with the guardrails intact.
GitHub Copilot, Cursor, and Claude rolled out across teams with explicit human-in-the-loop review, architectural guardrails, security validation, and responsible AI engineering standards. Velocity gains that don't show up as quality regressions or three-month incident hangovers.
Reliability as a leadership responsibility.
CI/CD modernization, infrastructure-as-code, observability with teeth (Sentry, PostHog, SLOs that actually move decisions), incident management discipline, on-call hygiene, and an MTTR story that the business can plan around. Heroics are a symptom; systems are the fix.
Across very different industries, the obsession has been the same.
Whether it was a £5M rollout in London, a 15-program portfolio in Dubai, a 40-engagement studio at Builder.ai, or a 60-engineer AI-first organization at Sequifi — the work has always been the same shape underneath. Build the systems that let smart people do their best work without burning out. Make delivery predictable. Make reliability boring. Make AI an amplifier, not a liability. Make the engineering organization a place where the next leader can land and lead.
I don't believe in 10x engineers, rockstar teams, or any framework that treats people like throughput. I believe in calm engineering, in operational maturity, in delivery governance that earns trust over time, and in AI-native execution that respects the human judgment it depends on.
If any of that resonates — for an organization you're scaling, an AI transformation you're navigating, or an engineering function that's outgrown its current operating model — I'd love to talk.