Capabilities

Technical depth across the full delivery path.

Production engineering across software development, systems integration, cloud and data platforms, LLM workflows, security, and technical delivery.

Software development

Production software built across systems, applications, interfaces, APIs, and data tooling.

  • Python and C++14 applications, components, APIs, and diagnostic interfaces
  • Desktop and web application development, including UI/UX wireframes and interface design
  • Data tooling, debugging, profiling, and maintainable build workflows

Languages, frameworks, and development tools

  • Python, C++14
  • Django, React, Qt5
  • Pandas, NumPy
  • Linux / Unix (Ubuntu, CentOS)
  • GCC, GDB, Valgrind, CMake

Systems & integration

Connected systems that must respond quickly, work reliably, and remain maintainable.

  • Low-latency C++ components and high-volume message routing
  • RESTful APIs, microservices, market data connectivity, and C++ / Python bindings
  • SSO and identity integration, local persistence with deferred sync, load balancing, and redundancy
  • Embedded systems programming and USB device controls
  • Latency measurement frameworks and performance analysis

Cloud & data

Cloud modernization and data pipelines designed around integrity, traceability, and operational needs.

  • Workload rehosting, cloud architecture redesign, and services authorized for government use
  • ETL architecture and integration across multiple data sources, with schema inference and evolution
  • Medallion architecture, checksum validation, and measurable data quality
  • Satellite ephemeris ingestion and launch-detection data workflows
  • Containerized services, infrastructure automation, and production monitoring

Platforms and data stores

  • AWS ECS and EKS
  • Apache Spark, Databricks Delta Lake
  • MongoDB, PostgreSQL, KDB, Vertica, Redis
  • Kubernetes, Docker, Terraform
  • CloudWatch, CloudTrail

LLM engineering

Model fine-tuning, retrieval, evaluation, and private inference built around specific engineering tasks.

  • Local LLM training and QLoRA fine-tuning workflows
  • Retrieval-augmented generation (RAG) and document analysis and scoring pipelines
  • Agentic workflow design, tool orchestration, local model serving, and private inference
  • Evaluation of models, prompts, retrieval, and outputs for repeatable results

Model training and inference software

  • Hugging Face Transformers, Datasets, PEFT, TRL, Accelerate
  • PyTorch, bitsandbytes
  • Ollama for local inference

Security & operations

Security, testing, and observability that help teams find problems and operate software with confidence.

  • OWASP penetration testing, NIST-aligned password policies, and security headers and ciphers
  • Code protection and obfuscation, Kubernetes license integrity controls, geofencing, and BSSID controls
  • Data validation before release, test libraries and coverage suites, and compliance control mapping
  • Performance and latency instrumentation, automated connectivity verification, and operational diagnostics interfaces
  • Embedded QA scenarios and consistent delivery pipelines

Delivery and monitoring tools

  • GitHub CI, GitLab CI, TeamCity
  • Splunk, Grafana, DataDog

Technical delivery

Requirements, architecture, implementation, testing, and stakeholder decisions carried through to delivery.

  • SSO requirements, Jira decomposition, implementation, and testing
  • AWS migration leadership and dependency coordination across teams
  • Architecture comparisons for SEC decision makers, phased implementation plans, and cost analysis
  • Systems testing with Army stakeholders, Apache flight analysis, and AH-64D test systems
  • Technical documentation, architecture presentations, and cross-team training