AI-Augmented DevSecOps & Secure Cloud-Native Infrastructure Expert transforming financial security at global scale
I build AI-driven security and DevSecOps automation for cloud-native financial infrastructure at PayPal. I am an IEEE Senior Member, author of 30+ publications, and an active peer reviewer and program committee member across IEEE and ACM venues.
I'm a Member of Technical Staff, Software Engineer at PayPal with over 10 years of experience designing and delivering secure, scalable cloud-native applications across the entire technology stack. Currently, I architect AI-driven security solutions for PayPal that safeguard 50+ million daily transactions and $1.53 trillion in annual transaction volume.
My expertise spans AI-driven threat detection, DevSecOps automation, cloud-native infrastructure (Kubernetes, microservices), and critical infrastructure protection. I'm passionate about building secure systems, leading cross-functional teams, and mentoring the next generation of engineers.
As an active researcher and IEEE Senior Member (top 10% of 500,000+ members), I contribute to the academic community through 30+ peer-reviewed publications, book chapters in leading volumes from Springer Nature and IGI Global, and extensive academic service including program committee memberships for IEEE, ACM, and international conferences. I also serve as Chair and Organizer of the ACM Austin professional chapter (895+ members, 4.8/5 rating) and as a judge for prestigious competitions including NASA Space Apps Challenge, MassChallenge, and cutting-edge hackathons. My research spans AI-driven security automation, autonomous cybersecurity systems, and zero-trust architectures with practical applications in enterprise environments.
Education: PhD Candidate in Information Technology, University of the Cumberlands (Expected May 2026) | M.S. in Computer Science, Texas State University
Selected roles across financial services, retail, and cloud-native platforms, focused on measurable outcomes and production impact.
Case studies highlighting problem context, approach, and measurable outcomes. Each item links to a demo or repository where available.
Problem: Multi-agent systems need secure identity, discovery, and trust routing across cloud-native environments.
Approach: Built a Kubernetes-native trust layer supporting MCP and A2A patterns with secure discovery and routing primitives.
Outcome: Enables reproducible agent identity + routing patterns and a clear demo path for platform teams adopting agentic systems.
Problem: Security teams need reliable AI assistance without high hallucination rates.
Approach: Implemented an evaluation-driven RAG workflow with reproducible experiments and governance-oriented outputs.
Outcome: Reported strong F1 performance with low hallucination profile, packaged as reproducibility code for research and practitioners.
Problem: Static authentication is brittle; security posture should adapt to risk signals.
Approach: Built a dual-agent, ML-driven risk-adaptive workflow suitable for digital banking scenarios.
Outcome: A reusable reference architecture for teams implementing risk-adaptive auth and policy-based controls.
Problem: Teams need a practical path from model artifact to scalable serving on Kubernetes.
Approach: Built step-by-step demos covering serving primitives, deployment workflows, and operational best practices.
Outcome: A repeatable learning and onboarding resource for platform and ML teams adopting Kubernetes model serving.
Problem: Modern pipelines fail due to configuration drift, operational complexity, and slow remediation loops.
Approach: Implemented self-healing and predictive maintenance patterns with security-by-design workflow guidance.
Outcome: Practical reference implementation demonstrating how AI can reduce toil and improve deployment reliability.
Recognized for outstanding contributions to technology and innovation in the industry.
First Author - Cloud Security Research
First Author - LLM Framework Research
First Author - Best Paper in track
First Author - Recognized among 13 accepted papers at conference
Innovation Excellence - 2nd Runners-Up | Led AI specFixer project
Top 10% of 500,000+ global members | Official elevation letter from IEEE President
Semifinalist - Long Island University School of Business
Comprehensive skills spanning full-stack development, AI research, and secure cloud infrastructure.
Contributing original research through peer-reviewed conference papers and journal publications in AI-driven security, DevSecOps, and cloud-native infrastructure.
Digital Health Journal
The AI Journal
IEEE International Conference on Computer and Applications
IEEE ICCA 2025
IEEE ICCA 2025
IEEE ICCA 2025
ACM Conference
IEEE Conference
ICAIC 2026
Contributing to leading academic publications from prestigious publishers including Springer Nature and IGI Global on cloud computing, AI security, and automated software engineering.
Springer Nature
Comprehensive chapter exploring AI-driven DevSecOps solutions for cloud infrastructure, focusing on automated security, policy enforcement, and secure deployment strategies in multi-tenant environments.
Full title: AI-Enabled DevSecOps for Secure Cloud Deployments
IGI Global – Predictive Analytics and Risk Intelligence in AI-Driven Cybersecurity
Book chapter on building predictive risk intelligence pipelines for AI-Augmented DevSecOps in cloud-native environments.
Full title: Predictive Risk Intelligence for AI-Augmented DevSecOps in Modern Cloud Architectures
IGI Global – Predictive Analytics and Risk Intelligence in AI-Driven Cybersecurity
Framework chapter describing risk-adaptive AI-Augmented DevSecOps for securing cloud-native systems.
Full title: AI-Augmented DevSecOps for Predictive Cloud-Native Cybersecurity: A Risk Intelligence Framework
Springer Nature Monograph
Comprehensive monograph on AI-powered DevSecOps for cloud-native systems. Under contract with Springer Nature.
IGI Global – Cybersecurity for Industrial IoT in Hybrid Cloud Environments
Chapter on deployment frameworks, strategic advantages, and security considerations for industrial IoT workloads running on hybrid cloud infrastructure.
Full title: Designing and Securing Hybrid Cloud Infrastructures – Deployment Frameworks, Strategic Advantages, and Security Considerations for Industrial IoT
IGI Global – Harnessing AI, Machine Learning, and Autonomous Agents to Combat Cross-Industry Threats
Chapter describing AI-augmented decision workflows for detecting and mitigating cross-industry security threats in cloud-native platforms.
Full title: AI-Augmented Decision-Making to Combat Cross-Industry Security Threats in Cloud-Native Environments
Contributing to the academic and professional community through peer review, program committee memberships, and judging prestigious competitions.
Web of Science verified profile: ResearcherID NWG-9697-2025
Leading global conversations on AI innovation, security, and DevSecOps through keynotes, conferences, and thought leadership platforms.
Expert Speaker & Panelist
One of five expert panelists discussing AI and data science innovations.
Featured Speaker
International organization (19,945+ LinkedIn followers).
Featured Guest
29,300+ YouTube subscribers.
Featured Guest
Palo Alto Networks podcast.
Speaking Engagement
IEEE Computer Society event.
Featured Speaker
Conference session on Agent Name Service (ANS) for secure AI agent identity and trust in cloud-native environments.
A showcase of impactful projects spanning security systems, scalable platforms, and enterprise applications.
Architected security controls protecting 50+ million daily transactions and $1.53 trillion in annual transaction volume. Reduced false positives by 30%, automated remediation for 60% of security incidents, achieved 20% latency improvement and 30% error rate reduction.
Engineered AI-powered automation frameworks compressing migration timelines by 75% (24-36 months to 6 months) with 99.8% first-attempt success rate. Documented $2+ million in cost savings and established methodologies adopted across financial services.
DNS for AI Agents - Kubernetes-native trust layer for AI agent identity and security. Open-source implementation enabling secure multi-domain collaboration.
Production-grade open-source projects advancing AI agents, DevSecOps, and cloud-native infrastructure. Many are backed by active research and published work (including SEDE and ICCA 2025).
I'm always interested in discussing new opportunities, technical challenges, or potential collaborations.
Deep-dive conversation on AI-Augmented DevSecOps, agent identity, and secure cloud-native infrastructure.
Guest Speaker
Discussing Agent Name Service (ANS), zero-trust architectures for AI agents, and practical patterns for securing large-scale cloud-native systems.