16+ years shipping production-grade AI platforms, scalable cloud systems, and high-velocity engineering teams across HRTech, HealthTech, IoT, and public sector — in India and South Korea. I advise at the strategy level, and I also personally build — writing code, shipping web and mobile apps, and standing up production infrastructure.
I believe great technology is human-centred by design — built with Logic to solve real problems, Empathy to serve real people, and Curiosity to keep asking better questions.
Currently: CTO at Noble House Consulting & OEAT
Selected Case Studies
Led architecture, hiring, and delivery for an AI workflow product — from zero to paying customers with eval harness, guardrails, and SOC2-ready foundations.
Key result
14 weeks → production
Designed a three-layer resilience model and automated eval suite for AI agents handling recruiter workflows — cutting live incidents and rollback frequency.
Key result
40% ↓ incidents
Architected voice and agentic workflows for resident services in senior-living communities — with trust boundaries, human escalation, and compliance-aware data handling.
Key result
<2s agent response
My Expertise
Services
Align tech with KPIs; ship decisions, not decks.
Learn more →From prompt to production with evals & guardrails.
Learn more →Custom web & mobile apps, WordPress sites, and Shopify stores—from MVP to enterprise scale.
Learn more →Most engagements start with a free 30-minute call. View all services →
Capabilities
Building outcomes, not artifacts. I focus on small, end-to-end loops— ingest → infer → evaluate → iterate—with guardrails and observability from day one.
Align tech with KPIs; ship decisions, not decks.
From prompt to production with evals & guardrails.
Design modular services with clear SLAs and cost/perf budgets.
Zero-downtime moves, IaC, and pragmatic reliability.
Close gaps fast—prove it with evidence packs.
Rituals, standards, and feedback loops that raise velocity.
Cross-platform apps that scale from MVP to enterprise.
Writing
Sharing insights on AI, platform architecture, and technical leadership through books and articles.
Stories of love, struggle, and acceptance in India
Personal and observed accounts of LGBTQ+ lives in India — navigating identity, family, and belonging in a society mid-transition.
Tales inspired by seven years in South Korea
Vignettes from the intersection of two cultures — an Indian technologist finding his footing, friendships, and fascination in Korea.
Real engineering outages and what they teach
Postmortems told honestly — the cascading failures, human errors, and systemic gaps that no monitoring dashboard caught in time.
Want to discuss any of these topics or need guidance on similar challenges?
Research
Contributing to the tech community through research, conference talks, and thought leadership.
Preprint (Zenodo) · 2026
The CAP Theorem and its PACELC extension provide principled frameworks for architectural trade-offs in distributed data systems. This paper proposes CAAP — extending that reasoning to AI systems making consequential decisions about people in employment, healthcare, and financial services. Accountability is treated as a first-class architectural constraint alongside Consistency, Availability, and Partition Tolerance: it constrains architecture, forces unavoidable trade-offs, cannot be retrofitted after deployment, and can be operationally verified.
Organizations are rapidly delegating managerial functions—including candidate screening, performance evaluation, workforce planning, and disciplinary risk assessment—to artificial intelligence systems. This paper introduces the AI Manager Decision Authority Framework (AI-MDAF), a conceptual governance model that allocates decision authority between humans and AI across the employee lifecycle, and the Named Human Principle: every consequential AI-assisted managerial decision must resolve to one specific accountable human.
A practitioner's framework for building resilient AI agent systems — covering 6 documented failure modes and a 3-layer resilience model for production deployments. Based on real-world observations across HRTech and HealthTech platforms.
Techniques and evaluation frameworks to reduce algorithmic bias in candidate ranking and screening systems.
Explores how traditional talent discovery breaks down in closed consultant ecosystems and presents a graph-based model for discoverability, contextuality, and matching — comparing linear queries with graph-enabled search, plus privacy and integration considerations for staffing and HR tech platforms.
Voice and AI agents in resident services — design patterns, failure modes, and trust considerations for vulnerable populations.
Interested in having me speak at your event or collaborate on research?
Newsletter
Thoughts on tech leadership, AI, and building products—every Friday. Read the latest below or follow on LinkedIn.
On Consistency, Availability, Partition Tolerance — and the constraint Eric Brewer never named.
Read on LinkedInThe story everyone's missing while they argue about AI.
Read on LinkedInThe biggest unsolved problem in AI product design isn't hallucination. It's miscalibrated trust.
Read on LinkedInAI didn't make technical skills irrelevant. It made the half-life of any specific skill shorter than it has ever been.
Read on LinkedInNew editions every Friday on LinkedIn.
View All NewslettersWhat Clients Say
“Diwesh turned our vague AI idea into a shipped product with measurable business impact — in weeks, not months. He brought architecture rigour we didn't know we needed.”
“The best thing about working with Diwesh is that he speaks both languages — deep technical architecture and clear business outcomes. Rare combination at the CTO level.”
Let's discuss how I can help you achieve measurable outcomes — from AI strategy to platform architecture.