I bring over 17 years of cross-functional leadership across Capital Markets and enterprise automation, with a recent emphasis on shaping governance frameworks for Generative AI within the financial technology sector.
{
"role": "Senior Architect",
"focus": [
"GenAI",
"Quality Engineering",
"FinTech",
"Agentic AI"
],
"experience": "Over 17 years",
"research": "Gen AI + Enterprise Systems",
"current": "Building what comes next"
}
I build at the intersection of enterprise engineering, Quality Engineering, Capital Markets and AI.
My career has evolved from software engineering and automation into architecture, transformation and AI-enabled engineering. My professional focus includes Capital Markets Technology, Quality Engineering and enterprise delivery.
Today, I focus on GenAI, agentic engineering, intelligent quality practices and the governance needed to make emerging AI useful in production — not just impressive in a demo.
Alongside industry work, I am pursuing an MBA in FinTech through BITS Pilani WILP and developing applied research around controllable AI systems, including Dynamic Inference-Time Knowledge Isolation (DIKI).
LLMs, RAG, AI agents, MCP-style orchestration, AI-assisted engineering and enterprise adoption patterns.
Enterprise test strategy, automation architecture, CI/CD quality gates, intelligent testing and QE transformation.
Technology and engineering experience across financial-services platforms, with current academic work in FinTech.
Applied research connecting emerging AI techniques with practical enterprise engineering and governance challenges.
Selected professional and research themes. Where a project or result is not publicly documented, the site describes it as a research direction or concept rather than a verified production deployment.
A reversible approach for selectively suppressing targeted knowledge pathways at inference time without permanently modifying model weights. The work explores controllability, isolation effectiveness, accuracy retention and inference overhead for smaller language models.
Exploring how GenAI, agentic workflows and intelligent automation can transform software testing, defect analysis, test design and engineering productivity in regulated enterprise environments.
Architectural concepts for routing enterprise knowledge and AI capabilities across tools, teams and delivery workflows while preserving governance and contextual relevance.
Innovation work spanning AI-assisted engineering and runtime reliability, including invention disclosures developed through enterprise technology initiatives.
My research interests focus on making AI systems more controllable, explainable and useful in real-world enterprise settings.
Discuss a collaboration →Dynamic Inference-Time Knowledge Isolation — reversible, selective knowledge suppression at inference time.
AI SYSTEMSAgentic and GenAI approaches for software quality, testing, engineering productivity and governance.
SOFTWARE ENGINEERINGAcademic exploration of digital financial ecosystems, business models and public digital infrastructure.
BITS PILANI WILPVerified certifications and professional learning shown on my public LinkedIn profile.
AWS
GOOGLE CLOUD
GITHUB
AZURE
SCRUM ALLIANCE
ORACLEAWSGOOGLE CLOUDGITHUBAZURESCRUM ALLIANCEORACLEAWSIssued Jun 2022 · Amazon Web Services
AWSIssued Sep 2022 · Amazon Web Services
GOOGLEIssued Jul 2025 · Expires Sep 2026
GITHUBIssued Mar 2025 · Expires Mar 2028
GOOGLE CLOUDIssued Jul 2022 · Public profile shows Jul 2025 expiry
MICROSOFTMicrosoft · credential listed publicly
Indian School of Business
SCRUM ALLIANCEScrum Alliance
ORACLEOracle
Issued Sep 2021 · NIESBUD
Recognition recorded on my public LinkedIn profile — kept separate from professional certifications.
Leadership recognition listed on my public professional profile.
Long-service recognition from Cognizant.
Performance recognition recorded on the public profile.
Innovation recognition recorded on the public profile.
Innovation recognition recorded on the public profile.
Idea and innovation recognition recorded on the public profile.
Idea and innovation recognition recorded on the public profile.
Enterprise AI will not be won by models alone. It will be won by the engineering systems, quality practices and governance that make those models trustworthy.
Writing about enterprise AI, software quality, governance, FinTech, product strategy and the changing role of engineers.
Governance for autonomous AI through policy grounding, continuous telemetry, human escalation and behavior-level control.
Read on Medium ↗Why token consumption changes the way AI systems need to be designed, measured and scaled.
Read article ↗A proposed engineering model for testing and observing agentic AI systems beyond conventional software QA.
Read article ↗Exploring structured, connected enterprise knowledge as an alternative lens to chunk-first retrieval.
Read article ↗Why engineering depth becomes more valuable when AI can generate increasingly sophisticated software.
Read article ↗Open to research conversations, speaking opportunities, technology discussions and meaningful collaborations around AI, Quality Engineering and FinTech.