Pre-screened and vetted.
Junior Full-Stack Developer specializing in web systems and performance-focused backend work
Mid-level SDET specializing in cloud-native test automation and API testing
Mid-Level Full-Stack Software Engineer specializing in AWS cloud-native web apps
Executive Technology Leader specializing in SaaS, cloud platforms, and engineering leadership
Senior Data Engineer specializing in cloud lakehouse platforms for banking and healthcare
Mid-level AI Software Engineer specializing in agentic AI, RAG, and data engineering
Mid Software Developer specializing in Java microservices and cloud-native systems
Entry-level product and frontend developer specializing in student platforms and FinTech
Staff Software Engineer specializing in distributed systems, blockchain, and AI/ML platforms
Senior .NET Full-Stack Developer specializing in healthcare and financial services
Mid-Level .NET Full-Stack Developer specializing in Azure cloud and SPA development
Intern Data Scientist/ML Engineer specializing in generative AI and ML platforms
“AI Engineering Intern at The Etherloop building the backend for a healthcare lifestyle recommendation app, including a multi-agent RAG-based system that uses curated SME data plus web search to generate personalized supplement recommendations from user lifestyle details and blood biomarkers. Evaluates against 500+ SME ground-truth profiles with ranking metrics and focuses on HIPAA-aligned deployment, privacy/security, and guardrails to reduce hallucinations and unsafe outputs.”
Mid-level Full-Stack Software Engineer specializing in web and SaaS applications
“Full-stack product engineer who independently built and maintained a marketplace app end-to-end, including React/Node/PostgreSQL architecture, real-time chat, S3 uploads, and OpenAI-based listing validation. Also shipped backend-heavy workflow features for a nonprofit upward mobility platform using Supabase edge functions, with a clear focus on security, role checks, and operational reliability.”
Junior Full-Stack Software Engineer specializing in cloud-native distributed systems
“Software engineer with JPMorgan Chase experience building a real-time operations console backend on Spring Boot/Kafka/Kubernetes and resolving peak-load latency through profiling, indexing, caching, and async processing. Also built and owned an AI-driven digital-archives metadata pipeline during a master’s at UNT using OCR + LLaMA-based prompting with validation, near-human accuracy, and human-in-the-loop guardrails.”
Senior Full-Stack Software Engineer specializing in Insurance, FinTech, and AI/ML applications
“AI/backend engineer who fine-tuned and deployed a production LLM chatbot using a LangChain + FAISS RAG pipeline, improving latency with PEFT/LoRA and driving strong business impact (40% customer adoption; 92% satisfaction). Also served as technical lead on a data aggregation system for underwriting/quoting, introducing GraphQL for more efficient, maintainable querying and applying CDC to keep cached ranking data fresh at scale.”
Staff Software Developer specializing in enterprise backend and event-driven systems
“Backend-heavy engineer with deep experience building enterprise and real-time systems across healthcare, operations monitoring, e-commerce, and 911 call center domains. He has led and personally coded greenfield and customer-facing platforms, including cloud/on-prem integrations, custom workflow tooling, and microservices architectures, while now independently upskilling into modern TypeScript/React-based frontend technologies.”
Senior Full-Stack Python & AI Engineer specializing in FinTech and real-time platforms
Executive Technology Leader (CTO/CPO) specializing in digital transformation, ERP, AI/ML, and M&A
Junior Full-Stack Application Developer specializing in internal web portals
“Frontend engineer experienced delivering React + TypeScript dashboards end-to-end, including real-time updates via WebSockets and scalable component architecture. Has improved inherited codebases by building shared component libraries and performance optimizations (lazy loading/memoization), reporting ~20% faster feature delivery, and ships major features with QA rigor and feature-flagged rollouts driven by analytics and user feedback.”