CORE COMPETENCIES • LLM Strategy (OpenAI, Bedrock, enterprise deployment models) • Agentic Systems & Orchestration (LangChain concepts, workflow design) • RAG Architectures & Enterprise Knowledge Systems • AI Platform Architecture (APIs, pipelines, integrations) • AI Governance, Guardrails & Responsible AI • Cost / Latency / Throughput Optimization • Cloud-Native AI Systems (AWS focus) • Observability, Monitoring & AI Lifecycle Management • GenAI Transformation & Enterprise Adoption • AI Governance, Risk & Compliance (Responsible AI, Guardrails) • AI Ops (Monitoring, Drift Detection, Root Cause Analysis) • Agentic AI & LLM Workflows (Amazon Bedrock, orchestration patterns) • Stakeholder Engagement (VP/C-Level) • Cost Optimization & Operational Efficiency

CORE SKILLS AI Tooling & Enablement • AI-assisted development workflows • AI tooling environments (POCs, demos, experimentation) • LangChain, LangGraph, LangSmith • Prompt engineering & AI workflow integration Context & Data Systems • API integrations & data pipelines • Knowledge systems & retrieval patterns (RAG familiarity) • Event-driven architectures Security & AI Governance • IAM, encryption (KMS), secure data pipelines • AI/GenAI security considerations • Compliance (NIST, CIS) DevOps & Automation • Terraform, CI/CD, Git workflows • Infrastructure as Code Programming & Tooling • Python (Strands SDK) • Internal tooling & automation scripts

PROFESSIONAL EXPERIENCE Self-Employed / Consulting Cloud AI Solutions Architect Specialist (AIOps) | 2025 – Present • Advise enterprise customers and executive stakeholders on AI and cloud transformation strategies, translating business objectives into scalable operating models, governance frameworks, and production-ready AI architectures. • Lead cross-functional delivery teams spanning architecture, platform engineering, operations, security, and business stakeholders to design and operationalize cloud-native AI solutions across regulated and enterprise environments. • Architect and deliver agentic AI platforms using Amazon Bedrock, AWS Strands SDK, LangChain, and LangGraph, enabling multi-agent orchestration, tool execution, memory management, observability, and autonomous operational workflows. • Own end-to-end delivery from strategy and roadmap development through architecture, prototyping, implementation guidance, production readiness, and operational optimization. • Designed and implemented enterprise AIOps capabilities leveraging Amazon Bedrock to automate drift detection, root-cause analysis, event correlation (CloudTrail), and remediation orchestration across multi-account cloud environments. • Established AI governance and operating standards including guardrails, prompt engineering, human-in-the-loop validation, evaluation frameworks, auditability controls, and responsible AI deployment practices. • Defined reusable AI platform capabilities spanning orchestration, observability, governance, automation, and self-service deployment models to accelerate adoption across multiple business domains. • Built retrieval-augmented generation (RAG) architectures and secure enterprise knowledge patterns incorporating data lineage, access governance, embeddings, contextual retrieval, and auditable AI outputs. • Directed architecture and implementation of cloud-native AI platforms integrating serverless and event-driven services including Lambda, APIs, workflow orchestration, and asynchronous processing patterns. • Established AI observability and operational excellence practices including evaluation pipelines, reliability engineering, drift monitoring, performance measurement, inference optimization, and production readiness controls. • Partnered with executive leadership and technical teams to define AI roadmaps, prioritize investment opportunities, align governance requirements, and drive measurable business outcomes. • Developed reusable accelerators, reference architectures, and enablement frameworks that reduced implementation complexity, improved delivery consistency, and accelerated customer adoption. • Delivered executive workshops, architecture reviews, and immersive enablement sessions to build organizational capability and influence enterprise AI strategy and operating models. • Led cloud governance and optimization initiatives focused on cost transparency, platform efficiency, security controls, and sustainable scaling across enterprise environments. • Acted as trusted advisor across business and technical organizations, influencing architecture decisions, aligning teams without direct authority, and driving consensus across complex transformation programs.

Amazon Web Services (AWS) Specialist Solutions Architect AIOps (SME) | 2020 – 2025 AWS Ops Specialist Solutions Architect
• Led enterprise customer engagements across Cloud Operations, Governance, and AI adoption, aligning technical architectures to measurable business outcomes and operational excellence. • Designed and implemented secure, scalable multi-account cloud architectures aligned to AWS Well-Architected principles, enabling governance, observability, and enterprise-scale operations. • Owned initiatives from concept through production deployment including architecture, prototyping, implementation guidance, rollout, and operational optimization. • Designed and operationalized AIOps and agentic workflows using Amazon Bedrock, integrating observability, CloudTrail event correlation, remediation automation, and governance controls. • Built AI-enabled operational platforms capable of analyzing telemetry, compliance findings, infrastructure events, and operational signals to automate root-cause analysis and remediation guidance. • Developed GenAI-powered automation solutions leveraging RAG architectures, embeddings, enterprise knowledge bases, and contextual retrieval integrated with Amazon Bedrock and Amazon S3. • Architected event-driven and serverless operational patterns using Lambda, Step Functions, APIs, SQS, SNS, and asynchronous orchestration workflows. • Established AI observability practices including evaluation frameworks, drift detection concepts, execution tracing, reliability monitoring, and production readiness controls. • Delivered executive briefings, Immersion Days, and technical workshops to accelerate secure GenAI adoption and influence enterprise AI strategy. • Built reusable deployment blueprints and implementation accelerators that reduced time-to-value and improved customer adoption velocity. • Supported regulated customers through governance-first AI operating models emphasizing auditability, compliance, secure access controls, and human-in-the-loop validation. • Acted as trusted technical advisor across customers, field teams, and account leadership to accelerate cloud modernization and AI operationalization. AWS Technical Field Community (TFC) Leader • Led AWS Technical Field Community initiatives to improve field execution, technical readiness, and customer delivery consistency across Solutions Architects and Specialists. • Built and scaled technical enablement programs including workshops, Immersion Days, architecture playbooks, training curricula, and field guidance. • Mentored Solutions Architects and technical professionals through shadow engagements, architecture reviews, and hands-on delivery coaching. • Created reusable enablement assets that standardized customer engagement models and accelerated solution adoption across regions and teams. • Drove technical consensus and alignment across distributed stakeholders without direct management authority. • Influenced organizational architecture standards through framework development, field guidance, and cross-functional leadership. • Partnered with account teams, specialists, engineering, and partner organizations to accelerate strategic opportunities and improve execution quality. • Evangelized cloud and AI operating models through blogs, architecture content, internal communities, and customer-facing thought leadership. • Acted as technical lead across multi-team initiatives coordinating priorities, execution milestones, and stakeholder alignment. • Developed and delivered partner and public training programs to scale technical capability and increase adoption. • Simplified complex cloud and AI concepts into repeatable, field-ready patterns that improved seller mindshare and customer outcomes. • Built communities of practice focused on mentorship, technical excellence, and operational leadership. AWS Cloud Foundations SME – Frameworks / Open Source / Architecture Strategy • Served as Cloud Foundations SME responsible for defining scalable operating models, governance frameworks, and foundational architecture patterns across cloud and AI initiatives. • Originated and evolved Cloud Foundations concepts that influenced broader AWS guidance and accelerator initiatives, including alignment with Landing Zone Accelerator, Security Reference Architecture, and GenAI adoption patterns. • Led development and publication of reusable cloud automation frameworks and operational patterns that evolved from proof-of-concept into repeatable deployment models. • Designed governance-first cloud environments integrating SCPs, logging, monitoring, identity controls, encryption, and policy enforcement for enterprise adoption. • Built reusable architecture accelerators, operational blueprints, and implementation assets that simplified adoption and improved maintainability. • Defined and operationalized cloud and AI enablement capabilities including governance, orchestration, observability, automation, and self-service deployment models. • Published architecture guidance, code samples, implementation artifacts, blogs, and open-source contributions adopted across customer and field organizations. • Developed Cloud Foundations Framework (CFF) integrating security, compliance, FinOps, operational excellence, and scalable cloud operating models. • Led cross-functional initiatives that unified fragmented technical workstreams into enterprise reference architectures and repeatable best practices. • Designed production-ready AI platform patterns emphasizing responsible AI, evaluation, observability, governance controls, and operational scale. • Influenced technical direction across architecture, security, networking, operations, and platform domains without formal authority. • Translated emerging AI and cloud capabilities into practical deployment frameworks used to accelerate customer outcomes.

Per Scholas (AWS re/Start) Cloud Technical Instructor | 2016 – 2020 • Designed and implemented secure cloud architectures across AWS environments, including VPC design, network security controls, and identity governance. • Managed vulnerability lifecycle processes including identification, triage, remediation, and reporting across infrastructure and application layers. • Developed automation scripts (Python, Bash) to streamline security operations, reduce manual toil, and improve response times.

United States Army IT / Cyber Operations Specialist | 2015 – 2022 • Supported secure IT operations and infrastructure management for mission-critical systems. • Contributed to cybersecurity initiatives including incident response, risk assessment, and system hardening.

United Nations Association of Southern New York (UNA USA) Cloud Administrator | 2012 – 2015 • Facilitated migration and modernization of digital assets to the cloud. Oversaw administrative functions, including: identify and access management, web administration, patching and updating servers. • Supported secure IT operations and infrastructure management for mission-critical systems, and contributed to cybersecurity initiatives including incident response, risk assessment, and system hardening.

EDUCATION Bachelor of Arts – Political Science (Minor: Computer Science) Rutgers University, New Brunswick, NJ

KEY PROJECTS AI-Powered Secure Grant Proposal Platform (AWS) • Built a serverless GenAI platform using AWS (Bedrock, Lambda, S3) with secure document ingestion and retrieval (RAG architecture). • Implemented IAM-based access controls and encryption (KMS) to secure sensitive nonprofit data. • Designed system with security-first principles, including auditability, data isolation, and secure API exposure. AI Ops for Control Tower (GenAI-Driven Governance Automation) • Designed agent-based workflows to detect configuration drift and automatically recommend remediation • Integrated SNS, CloudTrail, and Bedrock for contextual AI analysis • Reduced manual triage effort and improved response time for governance issues Enterprise GenAI Enablement Framework • Defined scalable patterns for adopting LLMs within regulated environments • Implemented layered governance: Org-level guardrails; Sandbox innovation environments; Production validation pipelines Enterprise RAG + Agentic Workflow Platform • Designed workflows combining retrieval systems with LLM reasoning • Integrated enterprise data sources with governance and audit controls • Optimized token usage and response latency for cost efficiency AI Governance & Guardrails Framework • Built layered AI governance model ensuring compliance and safe deployment • Implemented monitoring and validation mechanisms for production AI systems

Enterprise Cloud Security Hardening Initiative • Led security hardening across cloud environments by implementing least privilege IAM, network segmentation, and centralized logging. • Reduced attack surface and improved compliance readiness across multiple production systems.