Scope & Ownership
Team Leadership
Led cross-functional engineering teams through complex platform launches. Coordinated frontend, backend, and infrastructure delivery to ship production systems on schedule.
End-to-End Ownership
Owned full-stack architecture for mission-critical systems. API design, database schema, frontend implementation, and deployment pipelines under single ownership.
Engineering Judgment
Made pragmatic tradeoffs balancing delivery speed, system complexity, and long-term maintainability. Scoped MVPs under real constraints, evaluated build vs buy decisions, and evolved architecture as product needs changed.
Technical Architecture
Designed distributed systems with serverless functions, microservices, and data workflows. Balanced scalability, maintainability, and delivery speed in system decisions.
Selected Case Studies
High-impact projects that scaled systems, improved workflows, and delivered value.
Account Registration Portal
Led team of 5 engineers to ship a production account registration system serving 100,000+ users, integrating React front-end with AEM and Java microservices.
Launched on schedule, enabled digital account creation at scale, improved conversion rates and user onboarding experience.
Rules-Driven Eligibility System
Architected and built a flexible eligibility and dynamic content platform using Next.js, Node.js, Redis, and Postgres to support complex business rules.
Reduced manual configuration effort by 70%, enabled rapid rule changes without deployments, improved accuracy of content targeting.
AWS Lambda Data Orchestration
Built TypeScript-based AWS Lambda workflows to orchestrate Java microservices, Snowflake queries, and S3 data pipelines for financial data processing.
Automated data flows, reduced processing time by 60%, improved reliability and observability of critical data operations.
ModelTriage
A context engineering platform for comparing, routing, and validating LLM outputs. Applies structured constraints and schemas to make AI workflows reliable at scale.
What It Does
ModelTriage applies context engineering principles to multi-LLM workflows: define structured inputs with explicit constraints, route requests to optimal models based on task requirements, validate outputs against schemas, and compare results across providers. Designed for production systems where reliability matters.
Intelligent Routing
Automatically route prompts to the optimal LLM based on task type, cost constraints, and performance requirements.
Diff & Compare
Run the same prompt across multiple models and compare outputs in a clean interface with structured diffs.
Schema Validation
Define output schemas, constraints, and validation rules. Enforce structure and catch errors before responses reach production systems.
Why It Matters
LLM outputs vary widely in quality, cost, and reliability. Context engineering brings system design discipline to AI workflows: measurable quality through validation, predictable costs through routing, and production confidence through structured constraints.
Tech Stack
AI in My Engineering Workflow
I use AI tools daily to accelerate delivery without sacrificing quality. My workflow integrates Cursor, GitHub Copilot, ChatGPT, Claude, and Gemini across code generation, documentation, review, and modernization.
Beyond individual productivity, I treat context engineering as a system design discipline: structured inputs, explicit constraints, schema validation, and repeatable workflows. This approach transforms LLM interactions from ad-hoc prompting into reliable, testable components that integrate with production systems.
Context Engineering
Design structured prompts, constraints, and validation schemas. Define success criteria and error handling. Build repeatable AI workflows that integrate with backend systems like any other service dependency.
Code Generation
Draft boilerplate, generate tests, and prototype features faster. AI handles the repetitive work while I focus on architecture and production logic.
Documentation
Generate API docs, write clear comments, and create onboarding guides. Keeps teams aligned and reduces knowledge silos.
Code Review & Migration
Catch edge cases, identify performance issues, and translate legacy systems into modern frameworks. Refactor technical debt with validation at each step.
Context Engineering in Practice
Built production AI systems using structured context engineering: defined input schemas, validation rules, constraint boundaries, and success criteria. Implemented with GitHub Spec Kit methodology for an internal translation tool.
Results: Consistent outputs, 70% reduction in iteration cycles, and predictable behavior under edge cases. The system handles thousands of requests with error rates comparable to traditional microservices.
Measurable impact: Reduced feature iteration cycles from days to hours for well-scoped work, enabling 3–5× faster delivery on AI-assisted features and significantly more time spent on system design and high-leverage decisions.
Experience
Lead Software Engineer
Blankfactor • 2023–2025
- Led team of 5 engineers to deliver account registration portal serving 100,000+ users
- Architected rules-driven eligibility platform with Next.js, Node, Redis, and Postgres
- Built AWS Lambda TypeScript workflows orchestrating Java services and Snowflake pipelines
- Applied context engineering principles to AI tooling: structured inputs, schema validation, and constraint-based workflows using GitHub Spec Kit methodology
- Accelerated delivery 50% integrating AI tools (Cursor, Copilot, ChatGPT, Claude, Gemini) into production workflows alongside traditional backend and frontend engineering
Senior Software Engineer
Citrix • 2022–2023
- Served as technical lead for enterprise web platforms (NetScaler, XenServer), owning end-to-end delivery and coordinating across cross-functional teams
- Led modernization of legacy AEM platforms, rebuilding Java services and React components to simplify architecture and improve performance and UX consistency
- Built embeddable applications and reusable product widgets using React, Node.js, and AWS serverless infrastructure
- Consolidated fragmented React and AEM components into shared libraries, increasing reuse and consistency across high-traffic sites
- Introduced automated testing and accessibility workflows (Jest, Axe), improving WCAG compliance and release confidence
Software Engineer
Citrix • 2020–2022
- Delivered large-scale web applications supporting multi-site, international deployments
- Led global rebranding and product renaming initiatives across 10+ applications and 27 international sites
- Built interactive purchasing workflows and reusable UI components using React, AEM, and REST APIs
- Promoted to Senior Software Engineer based on technical ownership, delivery consistency, and cross-stack impact
Tech Stack
Modern tools for building scalable, maintainable systems.