Federal production systems, shipped by the founder — every working day.

Bo Peng designed and deployed a live machine learning system at the a federal health agency (a federal health agency), a component of the U.S. Department of Health and Human Services, as a senior engineer embedded at a federal prime. The system serves real end users, processes real federal data, and passed full federal security review before deployment. This is founder credentialing — not a Precision Delivery Federal LLC contract.

Scope included:

• Across the stack ML pipeline from data ingestion through model serving
• Data preparation, feature engineering, and governance at federal health data scale
• Model training, evaluation, and validation against domain-specific benchmarks
• Production deployment with monitoring, alerting, and drift detection
• Security review artifacts and federal compliance documentation

Outcome: System cleared security review and went live. Operating in production today, serving real users as a mission capability.

Bo Peng built a data ingestion and analytics platform processing millions of federal health records as a senior engineer embedded at a federal prime. Real-time dashboards, automated reporting, HIPAA-compliant architecture. Supported downstream analysts and ML practitioners working mission health data problems. This is founder credentialing — not a Precision Delivery Federal LLC contract.

Scope included:

• Multi-source data ingestion (structured and semi-structured)
• Data quality, deduplication, and governance pipelines
• Warehouse design supporting both BI dashboards and ML feature engineering
• Real-time dashboards for operational visibility
• HIPAA-compliant access control, audit logging, and encryption

Bo Peng led migration of legacy applications to cloud-native architecture across multiple federal agencies as a senior engineer embedded at federal primes — not under Precision Delivery Federal LLC. Infrastructure-as-code, containerization, zero-downtime deployment.

Scope included:

• Legacy application assessment and wave planning
• Terraform-based infrastructure provisioning
• Containerization (Docker, Kubernetes) of monolithic applications
• Zero-downtime migration strategies with rollback paths
• Post-migration operational handoff and runbook authoring

Ranked in the top 0.1% of 200,000+ active data scientists on Kaggle, the world's largest competitive data science platform. Competitions span natural language processing, computer vision, structured data prediction, and ensemble methods.

Why this matters for federal work: Kaggle rankings are earned on held-out adversarial test sets, not self-reported benchmarks. A Top 200 rank is a direct signal of modeling skill under realistic conditions — wrong architecture choices, leaked features, overfit validation, and uncertainty mis-estimation all show up immediately in the ranking.