cover_letter / art_GnIa26xfbsY

Dear Counterpart Health Hiring Team, Counterpart Health is doing something genuinely difficult: making AI-enabled clinical decision support fit naturally into a physician's workflow, at scale, across value-based care arrangements. The gap between data arriving and data being *useful* — for providers, for analytics, for clinical products — is exactly the kind of infrastructure problem I have spent my career solving. At Intuit, I owned the platform layer that connected hundreds of microservices to 675M+ annual engagements; the lesson was the same one your JD articulates: every integration should make the next one faster, or you are building the wrong thing. **Technical and Platform Foundation** My platform work at Intuit is the closest analog to what this role requires. As Staff PM for Developer Frameworks and Platform Infrastructure, I owned the ICE Self-Service platform end-to-end — DevPortal, GitOps configuration, and the ICE Playground — and reduced developer onboarding from 2–3 weeks to minutes in pre-production and under 24 hours for production. That compression came from building reusable scaffolding, standardized templates, and monitoring frameworks, not from heroic one-off effort. I also initiated the MSaaS Drift Detection and Resolution program: I wrote a Java JAR library to scan Git repositories for configuration drift, partnered with Design on a remediation UI, and built a systematic remediation roadmap using OpenRewrite. The pattern — detect, surface, resolve, prevent recurrence — maps directly to the data health lifecycle your JD describes across claims, pharmacy, labs, enrollment, and provider alignment. At Splunk, I owned Search Service (Go microservices), Search Catalog (PostgreSQL metadata), and the Splunk Processing Language — and delivered the Scheduler Service end-to-end in roughly four months. I also led a query performance optimization initiative for a beta enterprise customer, building a mirrored topology for benchmark testing and achieving up to 10x performance improvements. Working directly with an enterprise customer's technical team to diagnose, reproduce, and resolve data-layer issues under production pressure is a skill I have exercised repeatedly. On the AI side, I built aeval, a local-first model evaluation platform with FastAPI orchestration, TimescaleDB, Redis job queuing, and a Next.js dashboard. The platform includes adversarial safety testing, data contamination detection via SHA-256 hashing, and statistical rigor — bootstrap confidence intervals, Welch's t-test, Cohen's d effect size — with CI/CD integration and automated regression gates. Building AI-first validation infrastructure that catches issues before they surface downstream is precisely the muscle this role requires. **Why This Role** The through-line in my career is owning the reliability of the data layer that clinical, analytics, and product teams build on — and recognizing that the infrastructure compounds only when you treat each integration as a system design problem, not a one-time project. Counterpart Health's focus on value-based care and chronic condition management means that data quality failures have direct clinical consequences. That accountability is the kind I want to carry. **Role-Specific Connection** The JD's emphasis on AI-augmented data operations resonates specifically: I have built multi-agent orchestration frameworks (OpenClaw), RAG retrieval pipelines with multi-provider LLM fallback routing, and automated visual evaluation systems that reduced evaluation cycles from 72 hours to roughly 4 minutes by repurposing existing pipeline infrastructure. I see AI as a force multiplier for exactly the triage, validation, and anomaly detection work that scales a growing integration portfolio — not as a future capability, but as something I have already operationalized. **Selected Prior Experience** - Delivered ICE Self-Service platform (DevPortal, GitOps config, ICE Playground), reducing developer onboarding from 2–3 weeks to minutes in pre-prod and under 24 hours for production, while mitigating $1M+ in projected opex growth — by building reusable patterns and monitoring frameworks, not bespoke solutions. - Initiated MSaaS Drift Detection and Resolution program: wrote Java JAR library to scan Git repos for configuration drift, partnered with Design on DevPortal UI, and built remediation roadmap using OpenRewrite — a systematic, scalable approach to data and configuration health. - Achieved 275% YoY growth in ICE engagements, scaling to 675M+ in FY23; scaled throughput from 6K to 50K TPS via rSocket migration supporting ~1.5M concurrent connections with sub-25ms TP99 — demonstrating the ability to own platform reliability at enterprise scale. - Led query performance optimization for a beta enterprise customer at Splunk, building a mirrored topology for benchmark testing and achieving up to 10x performance improvements — direct technical partnership with an enterprise customer to close a data-layer gap. - Built aeval evaluation platform with adversarial safety testing, data contamination detection, statistical rigor (bootstrap CI, Welch's t-test, Cohen's d), and automated CI/CD safety gates — AI-first validation infrastructure designed to catch issues before they reach downstream consumers. - Architected RAG retrieval pipeline with ChromaDB vector store, multi-provider LLM orchestration (Claude, GPT-4, Gemini) with fallback routing, structured output validation, and token budget optimization — production AI data pipeline with reliability and quality controls built in. - Managed concurrent execution across multiple product backlogs at Splunk using a repeatable RICE-based prioritization framework, balancing internal partner, third-party developer, and Fortune 500 customer requirements simultaneously. **Closing** Counterpart Health's mission — earlier diagnosis and longitudinal management of chronic conditions, delivered at the speed of software — depends on data that works reliably before it ever reaches a provider's screen. I want to build the integration infrastructure that makes that possible: playbooks, monitoring frameworks, AI-augmented validation, and the kind of enterprise trust that comes from showing up as a genuine technical partner. I would welcome the opportunity to discuss how my background maps to what you are building. Sincerely, **O. Felix Amoruwa** famoruwa@berkeley.edu | 909-731-9011 | felixamoruwa.info