Rayne K. Wilde (they/them)

On a defense-sector R&D team: enterprise ML/NLP and data-engineering platforms for government stakeholders; containerized delivery on protected networks and embedded systems.

• Architect containerized microservice platforms with Docker and Kubernetes, deployed via ArgoCD across protected networks
• Build ETL, OCR, NLP, and GenAI agent modules orchestrated with LangChain and Harness
• Integrate PostgreSQL with Apache AGE knowledge graphs for RAG, alongside Elasticsearch
• Deliver full-stack features with TypeScript/React front ends and Python back ends

Developed data-science solutions and containerized applications for government intelligence customers on secure networks. Concurrent with graduate study and research work, as most of my roles have been.

• Built a hybrid semantic-search system (vector ANN) as an Elasticsearch alternative, with phonetic matching and synonym handling
• Optimized knowledge-graph scalability and search relevance, reducing query latency on large datasets
• Developed unsupervised anomaly-detection models that alert analysts to emerging issues
• Implemented NLP extraction of nested locations with geocoordinate mapping
• Created an automated YARA scanner for vulnerability detection aligned to CVE and MITRE ATT&CK standards
• Designed the ontology and RAG architecture behind an interactive analysis platform

Machine learning tech lead for a data-science team delivering language-agnostic NLP analysis of unstructured data.

• Developed tone- and stance-classification models within sentiment-analysis pipelines
• Engineered a language-independent NLP ETL pipeline for multi-stage compression, normalization, and embedding
• Created a scalable document tagging and clustering platform supporting multi-objective analysis
• Improved storage efficiency through data cleaning, clustering, and deduplication

Intelligence NCO; produced and disseminated intelligence products for mission planning. Active orders (training and duty) through most of 2018-2021, drilling status thereafter.

• Produced, quality-reviewed, and disseminated intelligence products
• Automated recurring analysis workflows with Python, VBA, and Excel
• Mentored 3–5 junior Airmen and led new-Airman onboarding

Contract-to-hire placement (via staffing agency) for an NLP/data-science firm, building intelligence-analysis tooling.

• Automated recurring intelligence analysis with web-based tools built to client specifications, improving speed, reliability, and access to data
• Built ANN-based document-classification models with semi-supervised learning in a multi-stage pipeline for categorization, tagging, and cluster analysis
• Extended the pipeline with document summarization and clustering to surface insights across large document sets

Compiler internship on a trapped-ion quantum compiler: Qiskit/Python front end down to hardware-specific representations (QIR/LLVM), applying graph theory to routing.

• Applied graph-search algorithms to optimize ion routing within the compiler
• Built the foundation of an ion-movement simulation sandbox for configuring and running experimental setups

Independent data-science work: analytics, exploratory analysis, and reporting on application data.

• Produced exploratory and causal reports from application data
• Handled data cleaning, exploratory data analysis (EDA), visualization, and modeling

Text analytics and causal analysis linking work logs and corporate metrics to project-success probability.

• Built a machine-learning model linking work-log text (from SQL Server) to project-success probability
• Ran exploratory and causal quantitative analysis alongside qualitative review
• Classified records varying in both location and type

Led anti-harassment initiatives, coordinated weekly meetings, and recruited program members.