Mid-level Forward Deployed Engineer specializing in AI automation for finance and data platforms
RemoteForward Deployed Engineer2 years experienceMid-LevelEnterprise SoftwareFinancial ServicesFinTech
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About
LLM/agentic workflow specialist with healthcare deployment experience who has taken LLM-based automation from prototype to production using operator-in-the-loop validation, RAG-style retrieval, RBAC, and monitoring for sensitive data compliance. Demonstrated real-time incident resolution (retrieval timeouts due to network/proxy misconfig) and strong GTM support—hands-on developer workshops and sales demos translating technical safeguards and real-time ETL into measurable ROI (70% ops reduction, ~$200K/year savings).
THE GEORGE WASHINGTON UNIVERSITYmaster, Business Analytics & Artificial Intelligence (2025)
UNIVERSITY OF VIRGINIAbachelor, Computer Science & Technology Entrepreneurship (2022)
Key Strengths
Took healthcare LLM workflow automation from prototype to production with secure scaling for sensitive data
Validated LLM outputs via staging with anonymized data and side-by-side comparison to operator decisions
Built effective operator-in-the-loop feedback processes (shadowing workflows, mismatch flagging for retraining/prompt updates)
Real-time debugging of LLM/agent pipelines using logs/metrics/tracing and layer-by-layer isolation
Stabilized production incidents with pragmatic mitigations (caching, fallback prompts, circuit breakers) while implementing permanent fixes
Resolved live deployment failure by identifying retrieval timeouts from misconfigured network/proxy settings and restoring service without patient disruption
Strong technical enablement: runs hands-on workshops with live code and exercises to drive adoption
Sales partnership: translates technical architecture into business outcomes; supported enterprise healthcare deal with quantified ROI (70% manual ops reduction, ~$200K annual savings)
Led production deployment of AI workflow automation integrating legacy EHRs and multiple third-party APIs
Designed staged rollout with parallel environments using Kubernetes and Terraform
Built real-time ETL pipelines with MLflow while maintaining HIPAA compliance
Translated regulatory requirements into technical controls (encryption, RBAC, audit logging) in partnership with compliance officers
Systematic cross-layer troubleshooting (software, hardware, networking) to isolate root cause (misconfigured firewall throttling traffic)
Delivered customer-specific Python automations/ETL integrations reducing manual operations by 90%
On-site customer collaboration with aviation operators; iterated dashboards in real time based on operator feedback
Strong in cloud and backend architecture for scalable pipelines and automated workflows
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