Impact at Scale

Designed an AI-powered operational readiness agent using Anthropic Claude Opus to evaluate production readiness across enterprise cloud platforms supporting millions of users during the organization's highest-traffic seasonal event. The agent synthesized infrastructure telemetry, deployment status, monitoring data, operational documentation, and engineering health signals into actionable readiness assessments for engineering leadership.

• Aggregated operational data from AWS, CloudWatch, New Relic, Datadog, CI/CD pipelines, and engineering documentation
• Generated AI-powered production readiness assessments prior to major releases and seasonal events
• Identified infrastructure risks, monitoring gaps, deployment concerns, and operational dependencies
• Produced capacity planning, resiliency, scaling, and operational readiness recommendations
• Reduced manual review effort while improving consistency of engineering readiness evaluations
• Enabled engineering leaders to make faster, data-driven go/no-go decisions before critical production events

Built an AI-powered incident triage assistant that consolidated operational context from enterprise monitoring, alerting, documentation, and incident management platforms to accelerate incident response and improve engineering productivity. Rather than replacing engineers, the assistant prepared a complete operational analysis, enabling responders to begin investigations with comprehensive context already assembled.

• Integrated with PagerDuty, New Relic, Datadog, Confluence, runbooks, deployment history, and monitoring systems
• Automatically gathered incident telemetry, infrastructure health, recent deployments, service dependencies, and historical knowledge
• Generated structured incident summaries, probable impact assessments, investigation timelines, and recommended next steps
• Retrieved relevant operational runbooks, historical incidents, and engineering documentation
• Reduced manual context gathering during incident response, allowing engineers to focus on diagnosis and resolution
• Improved consistency, knowledge sharing, and organizational learning across incident response processes
• Significantly reduced manual investigation effort during major production incidents
• Reduced Mean Time to Resolution (MTTR) by providing AI-generated operational context

Built an AI-powered problem management system that collects operational data from Slack and Microsoft Teams messages, sends context to OpenAI GPT for analysis, and automatically constructs structured RCA pages, incident timelines, contributing factors, and stabilization steps — with action items documented in Jira for tracking and accountability.

• Aggregated incident signals and engineering communications from Slack and Microsoft Teams
• Sent consolidated operational context to OpenAI GPT for root cause analysis and pattern recognition
• Automatically generated structured RCA pages with timelines, contributing factors, and stabilization steps
• Documented action items directly in Jira for engineering follow-through and accountability tracking
• Reduced manual post-incident documentation effort and improved consistency of RCA outputs
• Strengthened organizational learning and resilience through structured problem management workflows

Designed and implemented an enterprise GitHub governance platform supporting 3,800+ engineering repositories, automating repository provisioning, compliance validation, CI/CD governance, and engineering standards across the organization.

• Automated provisioning and compliance for 3,800+ repositories
• Eliminated manual workflows via self-service automation with GitHub, ServiceNow, Slack, and Teams
• Generated audit-ready SOC2 and SOX evidence through automated CI/CD change management
• Enabled engineering teams to focus on higher-value work by reducing operational overhead

Directed operational readiness and release governance for enterprise SaaS platforms across AWS, Microsoft Azure, and Oracle Cloud Infrastructure, supporting millions of users across globally distributed engineering teams.

• Improved production reliability, deployment quality, and service availability at scale
• Established operational standards reducing risk across globally distributed teams
• Strengthened platform resiliency through proactive monitoring and capacity planning
• Increased release confidence through standardized operational readiness reviews

Developed executive dashboards providing real-time visibility into platform health, engineering throughput, deployment quality, incident trends, operational KPIs, customer impact, and compliance posture for senior leadership.

• Enabled data-driven decision-making across platform reliability and software delivery
• Provided real-time visibility into engineering KPIs and compliance posture
• Standardized operational metrics across Product, Engineering, and Executive Leadership
• Accelerated decision-making through unified operational intelligence

Led enterprise DevOps transformation initiatives modernizing CI/CD pipelines, deployment governance, and engineering operating rhythms across globally distributed engineering organizations.

• Modernized CI/CD pipelines improving deployment reliability and release velocity
• Standardized engineering operating rhythms improving cross-functional alignment
• Improved compliance automation reducing manual audit effort for engineering teams
• Championed continuous improvement increasing engineering productivity at scale

Developed an evaluation framework to assess frontier AI models across engineering and operational use cases, enabling data-driven model selection based on quality, reliability, latency, cost, and business outcomes.

• Defined standardized evaluation criteria for comparing Anthropic Claude, OpenAI GPT, and Google Gemini across incident management, operational readiness, engineering documentation, and decision-support workflows.
• Evaluated model performance using real engineering scenarios, measuring reasoning quality, factual accuracy, response completeness, consistency, latency, token utilization, and operational cost.
• Designed repeatable evaluation workflows using representative production incidents, runbooks, monitoring data, deployment history, and engineering documentation to validate model effectiveness before operational adoption.
• Established use-case-specific model selection guidelines, recommending the optimal model based on complexity, reasoning depth, response speed, and engineering workflow requirements.
• Partnered with engineering stakeholders to continuously refine prompts, contextual inputs, and evaluation criteria using human feedback, improving trust and adoption of AI-assisted operational workflows.
• Enabled engineering teams to confidently adopt frontier AI technologies while balancing performance, reliability, explainability, and operational efficiency.