I operate at the intersection of enterprise operations, risk governance, and AI-enabled decision architecture within large-scale environments. My work centers on designing operating models where accountability is explicit, escalation is disciplined, and judgment scales consistently across distributed teams.
I have led the integration of automation and AI-assisted validation frameworks into enterprise risk systems—strengthening preventive controls, reducing decision variance, and increasing defensibility in high-impact environments. My focus is not only on deploying intelligent tooling, but on embedding AI responsibly within governance guardrails that withstand regulatory and executive scrutiny.
Across investigations, marketplace integrity, and operational risk domains, I translate ambiguity into durable systems where human judgment and machine-assisted decisioning operate cohesively.
Professional Experience
Amazon — Manager, Risk Management | 2024 – Present
Accountable for enterprise risk, governance, and automation programs across marketplace integrity and operational controls within high-volume environments.
Own end-to-end responsibility for structured decision frameworks, escalation architecture, and cross-functional risk alignment spanning Legal, Finance, Product, Policy, and Technology stakeholders.
Representative Areas of Ownership:
• Design and scaling of automation-assisted validation workflows
• Formalization of escalation thresholds and review cadence
• Governance dashboards and executive reporting mechanisms
• Preventive risk pattern detection and control embedding
• Multi-layer team leadership across distributed operational units
• Reduction of escalation cycle time by approximately one-third through structured review redesign
• Led the integration of AI-assisted validation frameworks into risk review workflows
• Designed guardrails to ensure model outputs remained auditable and defensible
• Reduced manual review dependency while maintaining decision integrity
• Partnered with Product and Engineering teams on model governance and rollout strategy
Amazon — Investigations Leadership | 2021 – 2024
Led high-severity investigations involving fraud, abuse, and marketplace integrity, requiring consistent judgment under time constraints and incomplete information.
Built structured governance frameworks that strengthened decision defensibility and improved cross-functional alignment across Policy, Legal, and Technology partners.
Representative Areas of Ownership:
• Escalation governance and review standardization
• Investigation quality frameworks and performance scorecards
• Translation of recurring risk into upstream preventive controls
• Cross-team calibration to reduce decision variance by double-digit percentages
Earlier Career — Operations, Analytics & Team Leadership | 2012 – 2020
Held progressively accountable roles across operations management, analytics, and workforce optimization within performance-driven environments.
Built foundational expertise in KPI architecture, reporting integrity, and frontline performance leadership.
Representative Areas of Ownership:
• Workforce planning and intraday decision models
• Executive-ready reporting and dashboard development
• Performance management and team capability building
• Operational risk identification and process stabilization
Career & Leadership Context
My career progression reflects a deliberate expansion of scope—from frontline operational execution to enterprise-level governance and decision architecture.
Early experience in operations and analytics provided direct exposure to system behavior under stress. Observing performance gaps, escalation breakdowns, and metric distortion at the frontline shaped a long-term focus on structural clarity and measurable accountability. I developed an operating philosophy grounded in disciplined reporting, defined ownership, and transparent performance signals.
Transitioning into investigations and risk leadership shifted accountability from execution to judgment. Decisions increasingly required cross-functional alignment, principled escalation, and defensible reasoning under uncertainty. Over time, responsibility expanded beyond individual case resolution toward the design of operating systems that anticipate risk, reduce variance in outcomes, and strengthen organizational resilience.
This progression—from managing performance to engineering governance—has shaped how I approach leadership: clarity before complexity, structure before scale, and systems before symptoms.
Enterprise Experience
Experience operating within complex, global environments where operational scale, regulatory expectations, and marketplace integrity intersect.
Enterprise Governance Architecture:
• Designed escalation guardrails and structured review mechanisms across distributed teams to ensure defensible decision-making at scale.
• Established consistent governance cadence linking frontline operations to executive-level visibility.
Risk & Decision Frameworks:
• Built structured decision models for ambiguous scenarios where financial exposure and reputational implications required disciplined trade-offs.
• Reduced outcome variance across comparable cases by formalizing review thresholds and cross-team calibration practices.
Automation & Preventive Controls:
• Embedded automation-assisted validation workflows to strengthen control consistency without introducing operational drag.
• Transitioned focus from reactive investigation toward preventive detection patterns and systemic control reinforcement.
Cross-Functional Alignment:
• Partnered across Legal, Finance, Product, and Policy stakeholders to align operational risk posture with enterprise objectives.
• Facilitated structured executive reviews to ensure alignment between governance standards and operational realities.
AI Strategy & Governance Architecture
Led the strategic application of AI and automation within enterprise risk and operational governance environments.
AI-Enabled Decision Systems:
• Integrated AI-assisted pattern recognition into risk detection and investigation workflows
• Designed structured review models combining machine outputs with human judgment calibration
• Reduced decision cycle time while improving consistency across distributed teams
Governance & Responsible Implementation:
• Established control frameworks ensuring model transparency, traceability, and audit readiness
• Defined escalation pathways for edge-case and high-severity decisions
• Embedded monitoring mechanisms to validate model performance and mitigate drift
Enterprise Integration:
• Partnered with Product, Engineering, and Policy stakeholders to align AI implementation with enterprise risk posture
• Ensured intelligent tooling strengthened governance rather than bypassing it
How I Lead
My leadership approach is anchored in structural clarity. Before pursuing performance targets, I ensure that decision rights, escalation pathways, and review mechanisms are explicit. Teams perform sustainably when accountability is visible and judgment criteria are shared.
In ambiguous environments, I combine structured analysis with principled judgment. Not all decisions can be reduced to metrics; however, the process by which they are made must remain consistent and defensible. I emphasize early alignment across stakeholders to reduce downstream conflict and ambiguity.
I invest heavily in operating cadence—clear review rhythms, transparent metrics, and predictable escalation frameworks. Durable performance is rarely the result of intensity; it is the result of disciplined systems that allow capable teams to operate with autonomy inside defined guardrails.
In AI-enabled environments, I prioritize responsible integration over rapid deployment. Intelligent systems must operate within clearly defined guardrails, with human oversight embedded in high-impact decision pathways. Sustainable performance emerges when technology amplifies disciplined governance rather than replacing it.
Education
Post Graduate Diploma in Computer Applications
Swami Vivekanand Subharti University, Meerut — 2021