Vetted Software Engineers in the DFW Metroplex

“JavaScript engineer with open-source library contribution experience, including diagnosing a validation-related bug, shipping a tested fix, and improving documentation with practical examples and edge-case guidance to reduce repeated community questions. Emphasizes profiling-driven performance work, small safe refactors, and proactive ownership in fast-moving, unstructured teams.”

“Backend/AI engineer who has built and operated production Node.js/Express services on AWS (Postgres/Redis) and has hands-on experience shipping an AI-powered support agent using RAG (Pinecone + LLM) with grounding checks and evaluation for hallucination rate. Demonstrates strong production reliability/performance debugging, including reducing peak latency from ~2s back to sub-300ms through query and caching optimizations, plus designing agent workflows with retries and human-in-the-loop escalation.”

“QA automation engineer focused on backend/API quality who owned an end-to-end API regression suite and integrated Cypress/JS automation into GitLab CI with smoke-vs-nightly regression gating. Caught a critical auth security regression (expired tokens returning 200 instead of 401) before production and is strong in stabilizing flaky tests using network interception and actionable CI reporting.”

Milan Sharma

Screened

Mid-level Software/Systems Engineer specializing in Python, Linux, and network testing

Richardson, TX9y exp

“Entrepreneurial product builder who has shipped two live App Store apps (Pixo content-based product marketing platform and Clutch AI dating reply helper). Also helped build a real-estate seller platform end-to-end, using AI matching to find buyers and contributing to onboarding nearly paying users and generating active MRR.”

“Full-stack/ML-leaning engineer who has shipped production-grade real-time analytics and an internal AI support assistant using RAG over enterprise documentation. Demonstrates strong systems thinking across scalability, reliability, observability, and LLM safety/evaluation (thresholded retrieval, RBAC, response validation, regression-gated evals), with concrete iteration based on performance metrics and user feedback.”