4+ years building high-stakes backend systems in regulated finance, a domain I entered with
no prior financial-systems background. Founding engineer and backend lead of a client-onboarding
platform rebuild, where I designed the workflow orchestration engine, rules engine, and
compliance integrations, and earlier one of two primary production maintainers of a payments
platform that moved $106B+ without a missed settlement cut-off. I've taken three products from
proof of concept through production, and I lead the way I was mentored: staying hands-on while
growing the engineers around me.
Brillio — Client: Franklin Templeton, Wealth Management Technology · Remote from California on Eastern hours · Open to remote or hybrid roles · Contract Software Engineer, May 2022 – Jun 2025 → Software Development Lead (full-time), Jun 2025 – Present
Lead Software Engineer — Client Onboarding Platform (AOP 2.0)
High Net Worth Client Onboarding · Aug 2024 – Present
Founding engineer on a ground-up rebuild of the firm's high-net-worth client onboarding
platform; promoted to backend lead on the recommendation of senior architects. Led a team of
up to 5 engineers taking a heavily criticized legacy system from POC to MVP to production.
Platform's production release was recognized by leadership as a successful turnaround of a system users and stakeholders had previously criticized, validating both the rebuild's architecture and the team's execution.
Worked directly with the onboarding, compliance, and operations teams to turn their manual processes into software: automated over 60% of manual onboarding and KYC work and improved responsiveness 60–80% over the legacy platform.
Designed and built the platform's workflow orchestration engine, APIs, and data model on Spring Boot and SQL Server. When a major Camunda upgrade broke our Camunda-based build months before MVP, I made the call to replace it and rebuilt the entire workflow as a custom engine in one month.
The engine carries every request through validation, assignment, multi-team approvals, KYC review, and e-signature, with role-based authorization checked against each state transition, retries with backoff, and fault tolerance so no request ends in a broken state. New steps and approval paths are configured in the database, not deployed.
Architected the two core microservices along clear domain boundaries: a Drools-based rules engine governing all UI field behavior, with dynamic completion validation synced to the Angular UI for real-time, millisecond feedback, and a secure persistence service acting as the sole authorized gateway to internal systems.
Delivered the document collection and e-signature workflow: UI and backend validation of required documents by account type and client, document storage APIs on Azure Blob, and production support of the DocuSign integration, including resolving live e-signature incidents.
Built the KYC/AML screening API against LexisNexis from vendor documentation, returning screening hits for compliance review, as the proof of concept for the onboarding KYC integration.
Owned the data layer: Cosmos DB primary store (a generic recursive Patch-API save utility at 5–10 ms, working around a known SDK upsert limitation), SQL Server workflow state, and Snowflake. Built a Python/Streamlit application on Snowflake that converts legacy-platform data into business-ready documents, used weekly by the business to review and migrate legacy accounts.
Automated testing of onboarding flows with JUnit and Mockito; partnered with QA on workflow-scenario automation scripts.
Led the Java 17→21 migration ahead of the MVP release; sole backend engineer on-call during live-market hours; grew into full-stack delivery of Angular features alongside the Spring Boot services.
Mentored a summer intern new to enterprise Java through shipping several production APIs.
The persistence service directly reads and writes its own database (Cosmos DB and SQL Server), and separately calls domain services, the only outside microservices it's authorized to reach; those domain services pull from Snowflake. The persistence service itself also feeds the separate SWP Forms application. For one specific case (dashed), it also calls the rules engine synchronously and updates data based on the response. The rules engine itself never reaches domain services or Snowflake.
Backend Engineer — Rules Engine Service (Systematic Withdrawal Plans)
Aug 2024 – Dec 2025
Hand-picked by the lead architect to build a standalone Drools rules-engine microservice;
cut processing time for 500+ business rules from several seconds to 50–100 ms using fact
injection, agenda groups, and salience-based prioritization. Delivered the base
architecture and MVP in 3 months before handoff; later extended to power a second
production application. After handoff, onboarded the successor engineer and resolved
production issues through Dec 2025.
Mentored by a 20-year architect on a 3-person team; core contributor from POC through MVP to
production on the wealth management business's payments platform, across an 18-month
POC-to-MVP build and 2 years of enhancements. Became one of the team's two primary production
maintainers, personally overseeing live market-hours processing of transactions worth tens of
millions of dollars daily.
Owned the end-to-end financial processing lifecycle for $10M+ in daily transactions ($106B+ lifetime) against strict settlement cut-offs, never rolled to the next business day: bank confirmations, OFAC screening, AML monitoring, compliance reconciliation, and government audit-trail processing.
Built ACH, wire, and check processing: NACHA, wire, and positive-pay file generation and parsing, with Axway and Azure Blob file integrations.
Built idempotent transaction services so retries and replays could never double-post money, and eliminated production deadlocks (2–5 a day to zero) using optimistic concurrency control, retryable services, and custom thread pool executors.
Hand-built the platform's Spring/SQL Server transaction workflows and designed an event-driven architecture on Azure Event Grid powering 20+ monitored scheduled batch jobs, several on 5-minute cycles, including OFAC scanning and bank posting.
Implemented SAGA choreography over RabbitMQ for cross-service transactions, later migrating messaging to Azure Service Bus.
Built FIS Global Plus accounting, Cash Agent, external payment, and Azure-based check-reading AI integrations; later assisted the team's Java 8→21 upgrade.
Owned incident response and root cause analysis against SLAs, plus release sign-off; delivered on Azure with Kubernetes-based deployments, GitLab/Bamboo CI/CD, and ServiceNow incident and change tracking.
Led technical design discussions across cross-functional groups of 20–30 with product owners and business analysts.
About
What distinguishes my record is that every step of it was pulled, not pushed. I joined
Franklin Templeton as a contractor through Brillio; delivery on my first project earned a
two-year extension. The lead architect then hand-picked me to build the base architecture
for a second product. Senior architects and my manager recommended me for the lead role on
the flagship rebuild, and Brillio converted me from contractor to full-time employee in
June 2025. Each advancement came from people I'd delivered for asking to keep working with me.
That rebuild's production release was recognized by leadership as a turnaround of a
platform users and stakeholders had previously criticized.
That ownership extended to production itself. On the transaction-processing system I became
one of its two primary maintainers during live market hours, overseeing a process
that moves tens of millions of dollars a day and is unusually fragile: time-dependent and
strict on data, where any missed window or incomplete information meant a restart or a push
to the next business day's settlement. We never let a transaction spill over: coverage was
coordinated down to staggered lunches so one of us was always available, often in real-time
coordination with teams across the firm to post and settle through FIS Global Plus. That
same experience is what let me replace Camunda on the onboarding rebuild in a month with a
workflow engine of my own design.
I bring the rigor of regulated finance, things like KYC and OFAC compliance, government
audit trails, and systems where eliminating production deadlocks entirely is a requirement
rather than an achievement, and I hold my team to that same bar.
I try to stay a student first. I had no finance background when I joined Franklin Templeton,
and teaching myself banking, settlement, and compliance from zero showed me I can pick up
almost anything given enough time, so I keep pushing for the next thing I don't know yet
instead of settling into what I've already learned. AI is part of that now too. It's made
learning new tools and ideas faster than I expected, and I want to see how far that goes:
I'm currently working toward the Anthropic Claude Certified Developer and Microsoft Azure AI
& Agents Developer Associate certifications. Still, I'm careful with it: I use it to move
faster, not to skip the part where I actually understand what I'm building.
AI-Assisted Development
Since Franklin Templeton rolled out GitHub Copilot firm-wide in January 2026, I've ranked in
the top 5% of users on cost-efficiency — prompt spend versus code accepted — with over 30,000
lines of AI-assisted code accepted into production.
The practice I built around it is test-driven at its core. Test suites are written from
business requirements first; AI agents implement against them; and every change is gated by
agent-driven code review and agent-authored pull requests via GitHub MCP on Copilot CLI, so
AI-generated output is validated before it touches anything production-critical — under a
standing rule that existing functional tests are never broken.
I packaged that discipline into a shared copilot-instructions.md (architecture, coding
standards, code-comment conventions, and structured commit messages keyed to Jira ID,
causation, and rationale), custom CLI commands with Jira integration, and a Python tool that
aligns database schemas and data across environments — all adopted by other projects. I then
ran knowledge-transfer sessions on prompt and loop engineering, reducing token usage, and
optimizing agent efficiency that onboarded every engineer on my team and the leads of two
other product teams.
I first taught myself these workflows on personal time with Claude Code — parallel
git-worktree instances to move several parts of a project at once — and translated them onto the
firm-approved Copilot CLI as soon as it was enabled. That's also what let me grow from backend
lead into full-stack work, delivering Angular UI features alongside the Spring Boot services.
I use AI to move faster, not to skip the part where I actually understand what I'm building.
MultithreadingCustom Thread Pool ExecutorsOptimistic Concurrency ControlRetryable ServicesDeadlock Resolution
Testing & Process
JUnitMockitoJaCoCoRegression & API test automationPostmanRoot cause analysisIncident management against SLAsCode review & branching strategyTechnical documentationAgile/ScrumJiraWorking knowledge: PostgreSQL, Kotlin, Go, GraphQL
Education & Certifications
San Diego State University — B.S. Computer Science
In progress: Anthropic Claude Certified Developer – Foundations; Microsoft Azure AI & Agents Developer Associate
Get In Touch
I'd welcome the chance to talk about how I can contribute to your team.
