Josh Lowry — Platform Engineer, FDE

85%Enterprise
Adoption Rate

80%Workload
Reduction

5K+Users on
Platform

3Governance
Frameworks Built

Experience

Senior Software Engineer

2023 — Present

Clark Capital Management Group · Philadelphia, PA

Codified repeated deployment patterns into reusable integration, model-routing, auditability, and governance primitives that reduced time-to-value for downstream teams.
Designed reusable AI deployment patterns across model routing, document processing, auditability, and stakeholder review workflows — including the Automation Decision Framework, intentional friction model, and "When Do We Use AI?" governance visual.
Architected multi-model orchestration platform processing 5,000+ brokerage documents annually (Claude + Gemini) — designed for extensibility across custodians, not just the first use case. 95%+ accuracy, 40% infra cost reduction via intelligent model routing.
Engineered reusable serverless integration stack (Calendly · Microsoft Graph · Salesforce via AWS Lambda) — patterns now deployed across 60+ business units at 99.9% uptime.
Drove productization of internal AI tooling into external broker-facing offering — owned the "what should generalize vs. stay customer-specific" decision at every step.

Associate Director

2021 — 2023

Chubb Insurance · Philadelphia, PA

Led North America post-bind tooling across middle market ($1M+ revenue), major accounts ($1B+ revenue), healthcare, and international commercial lines — 16-person team, 85%+ enterprise adoption across thousands of users.
Built the accountability and adoption frameworks that turned working AI tools into things 5,000+ employees actually depend on. The engineering was table stakes; the governance was the hard part.
HIPAA-regulated and SOC 2-compliant environments — reliability, security, and governance weren't constraints to work around, they were the design requirements.

Systems Analyst → Senior Systems Analyst

2016 — 2021

Chubb Insurance · Philadelphia, PA

Architected company-wide underwriting platform for 5,000+ employees. Cut quote turnaround 60%. Built reusable automated procedures reducing manual workloads 80% across the audit review cycle.
Established training program for 20+ early-career associates — raising the engineering bar through documentation, mentorship, and repeatable patterns.

Research Analyst

2014 — 2016

Evergreen Eagle Capital · Equity & Private Equity Research

Built financial automation tools for portfolio risk assessment and daily performance reporting — reusable infrastructure that gave me early domain depth in investment workflows, data quality, and decision-support systems.

Also Building

MirrorMirror — LLaMA-based RAG model, 270+ facial landmark points, published to App Store & Google Play.

COINAILYZER — React Native AI coin-grading app (iOS & Android). Real-time on-device YOLOv8 Nano TFLite detection, automated obverse/reverse capture, backend grading workflows, subscriptions, and receipt validation. Live. Deep dive →

Building consumer apps teaches you what no enterprise project does: you can't fake knowing what good looks like.

Selected Deployment

Brokerage Document Intelligence

Embedded with operations, compliance, and engineering stakeholders to turn a manual brokerage-document workflow into a production LLM system. Owned discovery, technical scoping, model evaluation, deployment, and rollout. Built a queue-based AWS Lambda pipeline with encrypted document storage, Snowflake metadata/result tables, model routing by document class, source-grounded extraction, exception queues, reviewer feedback loops, and field-level evals. Result: 5,000+ annual documents processed at 95%+ accuracy, turnaround reduced from days to minutes, and 40% lower inference cost through model routing.

I don't just build tools. I build the frameworks that determine how organizations use them — the decision matrices, the accountability models, the governance structures that make AI adoption durable rather than fragile. That's the platform bet muscle the role description asks for.

I've shipped AI systems across the full deployment loop: discovery, scoping, model selection, evals, production rollout, stakeholder adoption, and post-launch iteration. My strongest pattern is turning messy regulated workflows into durable systems that balance cost, latency, quality, auditability, and user trust.

Automation Decision Framework · The Clark Exchange · 2025

Enterprise AI governance and systems architecture, built from inside regulated environments. At Clark Capital, I didn't just ship AI systems — I built the frameworks for how the firm uses AI. Automation Decision Framework, intentional friction models, "When Do We Use AI?" governance. In environments where HIPAA, SOC 2, and audit trails shaped every design decision.

"Turn repeated signals into platform bets."
Already doing it. Already shipping it.

Platform Primitive

Permission-aware context platform Source connectors · standardized retrieval API · eval registry · latency/error/cost telemetry · tenant isolation · rate limits · extension points for customer-specific workflow logic.

I standardize the hard platform concerns — permissions, connectors, retrieval, evals, telemetry — while leaving room for customer-specific logic at the edges.

Stack

AI / LLM

Multi-Model Orch. Prompt Engineering Claude API Gemini API RAG Agent Dev MLflow

Cloud & Infra

AWS Lambda S3 · EventBridge SQS / SNS CloudWatch Playwright

Integrations

Salesforce API Microsoft Graph Calendly Webhooks Box API

Data

Snowflake Databricks PySpark Python SQL

Governance & Compliance

HIPAA SOC 2 AI Governance Audit Trails RBAC Design Regulated Environments

Education

B.S. Accounting & Finance

Missouri Southern State University · 2014

Built For

Building platform capabilities grounded in real deployments — governance frameworks, reusable patterns, and architectural abstractions that hold up in regulated, high-stakes environments.

Systems where reliability, security, and governance materially shaped design — HIPAA, SOC 2, audit trails, and accountability structures aren't checkboxes here, they're the constraints that made the systems worth depending on.

Cross-functional technical leadership — engineering depth, executive communication, and the ability to translate complex tradeoffs into adoption decisions and measurable outcomes.