Chief Instructor — AI Platform & Infrastructure Engineering Lead - Tinkacode - Hybrid
(2025-12)
- Architected production-grade AI infrastructure curriculum for 200+ engineers, focusing on secure LLM API gateways, async agent orchestration, distributed RAG ingestion pipelines, and cloud-deployed AI services.
- Designed cloud-native cybersecurity and AI simulation labs enabling real-world attack/defense scenario testing, secure model deployment pattern validation, and infrastructure hardening under production-like load.
- Led technical mentorship on secure backend architecture for AI services, API protection, encryption-at-rest/in-transit, rate limiting, and DevOps automation, achieving 100% deployable project completion rate.
- Delivered infrastructure hardening and system design workshops, reducing production deployment errors by 40% through hardened CI/CD templates, environment standardization, and infrastructure-as-code guardrails.
- Guided system design for scalable microservices platforms, translating monolithic Django architectures into distributed patterns applicable to AI platform serving, vector search integration, and multi-tenant LLM access control.
Software Engineer — Distributed Systems & API Platform - Tinkacode - Contract
(2024-11 - 2025-12)
- Architected scalable backend infrastructure serving 5K+ daily active users, designing Django REST API platforms with JWT authentication, granular RBAC, and rate limiting — core security patterns for multi-tenant AI services and LLM API gateways.
- Engineered fault-tolerant webhook orchestration systems processing 1K+ daily asynchronous events with idempotency guarantees, exponential retry logic, and dead-letter queues — identical patterns required for LLM inference callbacks and event-driven agent workflows.
- Deployed containerized cloud-native services using Docker and GitHub Actions CI/CD, achieving 99.8% uptime and establishing infrastructure patterns transferable to model serving autoscaling and AI API gateway deployment.
- Optimized database query performance and connection pooling, reducing API latency by 35% and establishing patterns directly applicable to vector database performance tuning and sub-second semantic retrieval systems.
Full-Stack Engineer — Infrastructure & Security (AI-Focused) - State Department for Culture, the Arts and Heritage - On-site
(2021-09 - 2024-11)
- Engineered secure backend infrastructure supporting 50+ internal applications and 10K+ users, implementing network segmentation, secure audit logging, and RBAC — foundational controls for enterprise AI platform governance and LLM API access control.
- Automated network and server configuration with infrastructure-as-code principles, reducing deployment time by 60% and establishing reproducible environments for model deployment pipelines and containerized AI services.
- Conducted security assessments and penetration testing, closing 90% of identified vulnerabilities; expertise directly applicable to AI API security, prompt injection defense, and model access governance.
- Provided technical leadership and training on secure architecture standards, improving team compliance by 50% and establishing security-first engineering culture for sensitive data systems.
Freelance Software Engineer — Cloud & API Systems - Upwork - Remote
(2020-01 - 2023-08)
- Delivered 15+ production-grade backend systems for global clients using Django and Django REST Framework, architecting RESTful API platforms with JWT authentication and granular RBAC for sensitive data flows equivalent to AI API key management and user-scoped model access.
- Designed and optimized PostgreSQL and MySQL databases for high-concurrency workloads: advanced indexing, query plan analysis, and partitioning — skills directly transferable to vector database performance tuning, embedding store optimization, and sub-second semantic retrieval systems.
- Built fault-tolerant webhook and third-party API integration systems with idempotency, circuit breaker patterns, and async callback handling — core infrastructure for LLM service orchestration and real-time inference pipelines.
- Managed cloud-native deployments on AWS using Docker and CI/CD pipelines, maintaining 99.8% uptime across 500+ end-user systems; established monitoring, alerting, and rollback patterns essential for AI service observability and reliable model serving.
