Pre-screened and vetted in the Austin Metro.
Mid-level Full-Stack Software Engineer specializing in backend and AI systems
“Full-stack and AI engineer who has built both user-facing products and production LLM systems, including an AI log-classification and incident-triage platform that cut manual triage by 60%+ and improved routing accuracy by 35%. Also brings cross-functional systems experience from an AI-driven digital twin project spanning HPC, Unity, AR, and large-scale simulation visualization.”
Principal Cloud & Data Architect specializing in AI-enabled AWS platforms
Senior Software Engineer specializing in Unity game and VR multiplayer development
Junior Full-Stack Software Engineer specializing in React/Next.js and AWS
“Backend engineer with Paycom experience who deployed a TypeScript web app on AWS and is re-architecting Stripe webhook handling using Kafka for durable, high-throughput asynchronous processing. Also delivered a freelance Python solution for a hospital that ingested sensor API data, normalized inconsistent readings, generated reports, and sent threshold-based email alerts while collaborating directly with hospital staff.”
Senior Full-Stack Engineer specializing in scalable React/Next.js platforms
“Backend/data engineer with strong production experience across Python microservices (FastAPI) and AWS serverless/data platforms (Lambda, API Gateway, Glue, Redshift). Demonstrates reliability and incident ownership (rate limits, retries/circuit breakers, monitoring) and has delivered measurable SQL performance gains (12–15s to <800ms, ~60% CPU reduction). Seeking fully remote work and not open to relocation/onsite meetings.”
Junior AI/Software Engineer specializing in LLM agents, RAG, and full-stack ML systems
“Backend engineer who built an Emergency Alert System with Virginia Tech for the City of Alexandria, focusing on real-time ingestion, secure dashboards, and AI-assisted prioritization. Emphasizes high-stakes reliability with guardrails (hybrid rules+LLM, confidence-based fallbacks), scalable async processing, and defense-in-depth security (JWT/RBAC plus database row-level security).”
Mid-Level Full-Stack Product Engineer specializing in TypeScript/React, Java, and AI integration
“Full-stack product engineer who builds and owns production features across Next.js/React/TypeScript and Java Spring Boot, with strong Postgres data modeling and performance tuning. Has delivered measurable improvements (60%+ faster renders, 2s→100ms queries, 50% lower workflow latency) and built reliable Kafka-based workflows with robust observability (Prometheus/Grafana/Alertmanager) and high test coverage.”
Mid-level Software Engineer specializing in backend engineering and applied AI workflows
“Backend engineer with fintech/transaction-processing experience who built and optimized a Spring Boot + PostgreSQL + AWS service handling money transactions, resolving peak-traffic latency via query/index and connection pool tuning. Shipped an LLM-driven risk-flagging workflow integrated via a FastAPI Python service, owning prompt design, validation guardrails, monitoring, and human-in-the-loop escalation to reduce false positives and improve precision over time.”
Mid-level Site Reliability Engineer specializing in cloud infrastructure and Kubernetes
“Backend/infra-focused engineer who owned production systems for distributed ML experimentation (hyperparameter tuning across a cluster with GPU scaling, custom scheduling, and checkpoint-based fault tolerance). Also built and operated a low-latency log validation service using queued async workflows with idempotency, retries/backoff, and strong observability, plus experience building resilient Selenium-based browser automations for complex multi-step web flows.”
Mid-Level Software Engineer specializing in LLM applications, RAG, and OCR automation
“At Trellis, built and shipped a production multi-agent, authenticated GenAI chatbot for sensitive financial account inquiries (loan/payment lookups), using dynamic model routing to control latency and cost while improving accuracy. Implemented prompt-injection defenses (Meta Prompt Guard), RAG with LangChain, and LLM-as-a-judge evaluation; the system cut manual support call volume by 40%+ and was refined through close collaboration with QA-driven user testing.”
Mid-level AI Software Engineer specializing in ML services and agentic workflows
Mid-level DevOps/Cloud Infrastructure Engineer specializing in Kubernetes and AWS automation
Mid-level Full-Stack Engineer specializing in healthcare and education platforms
Mid-level Software Engineer specializing in backend systems, microservices, and AI applications
Junior Full-Stack Software Engineer specializing in cloud-native microservices
Mid-level Software Engineer specializing in ML, optimization, and robotics
Mid-level AI/ML Engineer specializing in GenAI and LLM agent workflows
Senior Software Engineer specializing in AI platforms and cloud-native systems