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Vetted Convolutional Neural Network (CNN) Professionals
Pre-screened and vetted.
Mid-level Software Engineer specializing in cloud microservices and ML systems
Mid-level Frontend Engineer specializing in React and enterprise web applications
Intern Full-Stack Software Engineer specializing in web apps, IoT systems, and applied AI
Intern Backend Developer specializing in AWS serverless and distributed systems
Mid-level Data Scientist specializing in computer vision and behavioral analytics
Mid-level Full-Stack/AI Engineer specializing in LLM microservices, RAG, and data pipelines
Junior AI/ML Engineer specializing in LLMs, RAG, and applied NLP
Entry-Level Data Scientist specializing in machine learning, NLP, and cloud analytics
Mid-level Data Scientist specializing in ML, NLP, and Generative AI
Intern Full-Stack & AI Engineer specializing in ML-driven mobile and data platforms
Mid-level AI/ML Research Engineer specializing in NLP, LLM agents, and multimodal systems
Mid-level Full-Stack Developer specializing in Java/Spring Boot, React, and AI/ML APIs
Mid-level Java Full-Stack Developer specializing in cloud microservices and AI/ML integration
Mid-level AI/ML Engineer specializing in fraud detection, credit risk, and NLP
Mid-level AI/ML Engineer specializing in Generative AI, NLP, and RAG systems
Entry-Level Full-Stack Engineer specializing in backend APIs and cloud architectures
“Full-stack AI engineer who has built and deployed multiple end-to-end LLM products, including an AI interview assistant, a multi-agent market research platform, and a policy document explainer. Particularly strong in productionizing agentic workflows, integrating tools like Whisper, Tavus, LiveKit, CrewAI, and LangGraph, and hardening messy real-world AI/document pipelines with validation, memory isolation, and fallback handling.”
Lalith Kambala
Junior Full-Stack Engineer specializing in AI-powered web applications
“Full-stack product engineer who has shipped AI-powered job board moderation and validation features end to end across React/TypeScript, serverless backends, and Postgres. Stands out for combining UX polish, LLM-backed workflow design, and reusable async infrastructure patterns to improve reliability, speed of delivery, and user participation.”
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.”
Mid-level Generative AI & ML Engineer specializing in LLMs, RAG, and MLOps