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Vetted Agentic AI Professionals
Pre-screened and vetted.
Junior ML Research Engineer specializing in AI agents and multimodal systems
Mid-level AI/ML Data Engineer specializing in MLOps and Generative AI
Mid-level AI/ML Engineer specializing in computer vision and generative AI
Executive CTO specializing in SaaS and Financial Services platforms
Senior AI/ML Engineer specializing in Generative AI, LLMs, and data platforms
Mid-level Software Engineer specializing in full-stack, cloud, and AI systems
Mid-level Data Scientist / GenAI Engineer specializing in LLM agents, RAG, and OCR
Mid-level Software Engineer specializing in Cloud, DevOps, and Generative AI
Senior Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level Software Engineer specializing in backend distributed systems
Senior Software Engineer specializing in AI/ML and cloud backend systems
Mid-level AI Engineer specializing in agentic LLM workflows and RAG systems
CharanTeja Kurakula
Entry-Level AI Engineer specializing in NLP and LLM-powered applications
“AI engineer who built an agentic, production-deployed LLM workflow for tobacco violation parsing and automated multi-case creation, using six specialized agents and a human-in-the-loop confidence-threshold routing design. Addressed data privacy constraints by generating synthetic datasets with LLM prompting, and orchestrated reproducible end-to-end pipelines in LangChain with robust testing and evaluation (precision/recall, micro-F1).”
Shravani Surendra Chandratre
Entry-Level Software Engineer specializing in AI, systems programming, and full-stack development
“Systems-focused C++ engineer who built a 32-bit CPU simulator end-to-end (custom ISA, full memory model, fetch-decode-execute loop) and solved tricky recursion/stack-frame correctness issues through heavy instrumentation and tracing. Has strong Linux and user-kernel boundary experience (procfs) plus modern build/test tooling (Docker, CI/CD, pytest), and is confident ramping quickly into ROS/ROS2 despite not having used it directly.”
Pavan Sai Reddy Pendry
Junior AI/ML Engineer specializing in GenAI, RAG, and full-stack ML systems
“Built a university campus assistant chatbot (BabyJ/WWJ) using RAG and agentic routing with a FastAPI + React stack and JWT auth, focusing heavily on production concerns like latency and reliability. Uses techniques like speculative prefetching, smart intent routing, and rigorous eval/testing (golden sets, regression, edge cases) while collaborating closely with campus admin/advising teams to iterate based on real user feedback.”