PROFILE
Software Engineer with 3+ years of experience building fast, reliable, and well-architected
backend systems. Strong background in cloud infrastructure, data pipelines, and developer
tooling, with a focus on automated evaluation, regression detection, and system correctness.
Currently pursuing an MS in Computer Science at the University of Copenhagen, with interests
in human–computer interaction, machine learning, and evaluating human-AI systems.
SKILLS
- Evaluation & Experimentation: automated eval frameworks, regression analysis, confidence intervals,
failure analysis
- Backend & Data: Python, Go, Java, PostgreSQL, Elasticsearch, message queues
- Cloud & Platform: Azure, Kubernetes, Terraform, Docker, Helm
- CI/CD & Dev Tooling: GitHub Actions, release automation, commit-based quality gates
- Observability & Reliability: Prometheus, Grafana, SLIs/SLOs, incident response
- Frontend & UX: TypeScript, React, internal dashboards
- Security: IAM/RBAC, Azure Key Vault, NIST SP 800-53
- Automation & OS: Bash, PowerShell, Nix, Linux
- ML Systems: model evaluation, anomaly detection, TensorFlow, LSTM
EXPERIENCE
DevOps Engineer — SAS
Jun 2022 – Aug 2025
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Owned the migration from on-prem infrastructure to Microsoft Azure using infrastructure-as-code
(Terraform, Helm), reducing deployment times by 70% and enabling rapid iteration across teams.
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Built and operated Kubernetes-based, multi-tenant platforms supporting 500,000+ users, maintaining
99.999% uptime through robust observability, alerting, and on-call incident response.
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Designed and implemented automated evaluation frameworks integrated into CI/CD pipelines,
enabling fast detection of regressions, systematic comparison of system behavior across releases,
and higher-confidence deployments.
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Developed schema-driven test and evaluation systems that allowed engineers to easily add new
scenarios, run evals locally or in CI, and drill into failures affecting real user workflows.
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Owned data pipelines backed by PostgreSQL, message queues, and Elasticsearch for 14 clients,
doubling ingestion throughput while improving correctness guarantees and operational reliability.
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Designed secure cloud environments using RBAC, IAM, and Azure Key Vault, implementing NIST SP 800-53
controls for public-sector clients.
Software Engineer — Virginia Systems & Technology
Jan 2019 – Aug 2020
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Built geospatial web applications in React for public-sector clients, improving real-time
situational awareness and analyst decision-making.
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Optimized live data ingestion pipelines using Elasticsearch preprocessing and caching,
reducing refresh latency by 50 ms.
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Implemented LSTM-based anomaly detection systems in Python and C++ for time-series data,
including evaluation of false positives/negatives and threshold calibration, reducing
detection errors by 20%.
Software Developer Intern — Cobb County School District
Aug 2016 – Dec 2016
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Designed and built a web-based content management system used by 7,500 educators across
110 schools, reducing update time by 90%.
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Developed a mobile behavior-tracking app in Swift and Java for special education teachers,
increasing logged reports by 40%.
EDUCATION
MS Computer Science — University of Copenhagen
Aug 2025 – Present
- Focus areas: Human-Computer Interaction, Machine Learning, evaluation of human–AI systems
- Active in the KU Lighthouse startup ecosystem
BS Computer Science — University of Tennessee
Aug 2017 – May 2022
- Double minor in Cybersecurity and Business Administration
- Hackathon winner and startup competition honorable mention
- Student ambassador supporting interview preparation and representing the university to 150+ employers