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Vetted Data Structures Professionals
Pre-screened and vetted.
Junior Full-Stack Developer specializing in Java/Spring Boot and React for FinTech
Mid-level Sales Development Representative specializing in outbound outreach and logistics operations
Mid-level Backend Software Engineer specializing in scalable microservices and cloud infrastructure
Mid-level Cybersecurity Professional with QA and software development background
Senior Graphic Designer specializing in UI/UX, branding, and digital marketing
Mid-Level Software Engineer specializing in Python backend and API automation
Mid-Level Full-Stack Software Engineer specializing in cloud platforms and security
Junior Software Engineer specializing in full-stack, systems, and AI development
Intern Full-Stack/Backend Developer specializing in web APIs and data processing
Junior Full-Stack Software Engineer specializing in cloud-native healthcare applications
Junior Full-Stack Software Developer specializing in backend APIs and cloud-native systems
Mid-level Embedded/Firmware Engineer specializing in RTOS and low-power IoT systems
Junior Software Engineer specializing in full-stack, mobile, and cloud systems
Mid-level Full-Stack Software Engineer specializing in Generative AI and cloud-native systems
Tony Barreto
Mid-Level Full-Stack Software Developer specializing in modern web apps
“Product-focused full-stack builder who has shipped and operated multiple production apps from scratch, including an e-commerce bakery delivery scheduler (with concurrency controls and timezone handling) and a real-time passenger music-request system for Lyft rides that hit and resolved YouTube API rate-limit scaling issues via debouncing and caching. Strong in React+TypeScript and Node.js/TypeScript backends, with solid PostgreSQL/PostGIS data modeling and performance tuning.”
Rukmini Pisipati
Junior AI/ML Engineer specializing in LLM automation and NLP
“Built and shipped a production LLM hallucination detection and monitoring pipeline using semantic-level entropy (embedding-clustered multi-generation variance) to flag unreliable outputs in downstream automation. Implemented a scalable async architecture (FastAPI + Docker + Redis/Celery) with strong observability (structured logs + PostgreSQL) and developed evaluation loops combining controlled prompts and human review; also partnered with non-technical stakeholders on AI-driven form validation/document processing.”
Dhairya Shah
Entry-level Machine Learning Engineer specializing in computer vision and systems
“ML-focused builder who has shipped an end-to-end income-class prediction product: built the data pipeline, trained models, deployed via Streamlit with a live UI, and tracked success via accuracy (84%), adoption, and latency. Demonstrates strong practical MLOps instincts (Docker/Streamlit Cloud, logging/monitoring, caching) and data engineering reliability patterns (schema checks, idempotency, retries, backfills) while iterating quickly in ambiguous, solo-project environments.”