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Vetted NumPy Professionals
Pre-screened and vetted.
Intern Software Engineer specializing in cloud infrastructure, DevOps, and AI-enabled systems
Intern Full-Stack Software Engineer specializing in Node.js, AWS, and scalable backend systems
Mid-level AI/ML Engineer specializing in Generative AI, NLP, and RAG systems
Junior Machine Learning Engineer specializing in Generative AI and LLM agents
Mid-level Generative AI Engineer specializing in LLMs, RAG, and agentic systems
Mid-level AI & Data Science professional specializing in MLOps, deep learning, and UAV research
Junior Full-Stack Developer specializing in AI integrations and LLM research
Mid-level AI/ML Research Engineer specializing in NLP, LLM agents, and multimodal systems
Junior Full-Stack Developer specializing in Django/React and cloud-native APIs
Gabe Hoing
Intern-level software engineer specializing in full-stack and data systems
“Built an AI agent management system in a senior design project to support cybersecurity analysts with gathering and triaging emerging threat intelligence from sources like CISA. Stands out for a thoughtful, production-minded approach to AI development, using specialized agents, strict output schemas, and deterministic controls to manage failure cases.”
Jeevan Kumar
Mid-level Full-Stack AI Engineer specializing in LLM systems and RAG
“Built and shipped a production "Campaign AI" multi-agent system (LangGraph) that personalizes B2B outbound emails at scale using Apollo.io prospect data, clustering-based segmentation, and 21 persona variants. Notably uncovered that high click rates were largely email security scanners and created a validated bot-detection/scoring pipeline (timestamps/IP/user-agent/click patterns), bringing reported engagement down from ~40% to a trusted 5–8% that aligned with real conversions.”
Mahaboob Johny
Mid-level Data Analyst specializing in SQL/Python analytics, ETL pipelines, and BI dashboards
“Data/AI practitioner who built a production LLM-driven healthcare claims analytics and dashboarding system to reduce avoidable ER visits—processing 1.4M+ claims, flagging 19% as non-emergent, and projecting ~$2.8M in annual savings. Demonstrates strong real-world LLM reliability and performance engineering (grounding, numeric validation, caching, materialized views, quantization) plus orchestration experience with Airflow and Azure Data Factory.”
Harshit Vashisth
Junior AI Engineer specializing in LLMs, RAG systems, and MLOps
“Robotics software engineer who built an end-to-end system ("justmatrix"), focusing on multi-agent orchestration and a multi-RAG retrieval backend/API. Has hands-on ROS experience, including a custom node for reliable high-frequency sensor data routing, plus deployment automation using Docker, Kubernetes, and CI/CD.”
Entry Software Engineer specializing in Generative AI and full-stack development
Junior Software/AI Engineer specializing in LLM agents and RAG systems
Jaiden Kettleson
Entry-Level Full-Stack & AI Engineer specializing in chatbots and web apps
“Data Science honors graduate (Maryville University) who has built Python/SQL backends and a capstone website handling sensitive user data. Emphasizes secure data handling (password encryption, secure database updates) and uses Git/GitHub Pages with CI/CD-style practices for managing and deploying changes.”
Entry-Level Software Engineer specializing in full-stack web development and cybersecurity
Mahad Asif
Mid-level Game Developer specializing in Unity, AR/VR, and cross-platform products
“Exceptionally young game developer who says he began at age 10 and was employed as a Unity developer at 13. He has hands-on experience building gameplay systems, VR/Web/mobile projects, Photon multiplayer features, and AI/ML experiments—including an open-source protein-model research pipeline reportedly under peer review at PLOS ONE.”
kireeti Sangarsh
Mid-level Data Engineer specializing in cloud ELT pipelines and analytics engineering
“Data engineer who has owned end-to-end ELT pipelines on Airflow + AWS (S3/Glue/Lambda) with Snowflake/Redshift, processing millions of records per day and tens of GBs via PySpark. Built strong data quality and reliability practices (40% quality improvement, 99%+ uptime), and also designed a resilient web-scraping system with anti-bot defenses and schema-change versioning plus REST APIs for serving curated data.”