BEST-FIT USE CASE
Building and maintaining Go-based backend platforms that integrate multiple LLM providers, asynchronous workers, semantic search, monitoring, and CI/CD delivery for SaaS or data-heavy products.
Not the primary fit: Pure product management, standalone frontend-only delivery, or research-heavy model training roles would be a weaker fit than backend implementation and AI service integration.
EVIDENCE HIGHLIGHTS
What supports the assessment
01Designed a Go backend with multi-provider LLM orchestration, automatic fallback, rate-limited workers, and provider metrics.
02Built asynchronous processing across several services with broker-based consumers, retry and backoff behavior, reconnection handling, and prefetch tuning.
03Implemented semantic text comparison using diffing, Levenshtein thresholds by page type, AI summarization, and vector-backed chat search.
04Configured QA and production Kubernetes deployments with CI/CD, error tracking, analytics instrumentation, and monitoring components.
05Developed cloud-gaming backend services in Go with broker and cache integration, cross-platform tooling, SDK integrations, analytics, and tracing.
06Built backend services for real-time pricing and event streaming using Go, WebSocket feeds, Kafka, relational storage, and cloud deployment.
07Delivered internal enterprise web systems, marketplace functionality, mobility workflows, and data-platform integrations across several domains.