Vetted Deep Learning Professionals

Richa Chanpuriya

Screened

Mid-level Backend/Agentic AI Engineer specializing in GenAI automation and RAG systems

Remote, India3y exp

“Built and shipped a production AI-driven privacy automation system that autonomously navigates data broker sites to submit opt-out/data deletion requests end-to-end, including robust CAPTCHA detection/solving (e.g., reCAPTCHA/hCaptcha/Cloudflare) via 2Captcha. Experienced in orchestrating stateful LLM agent workflows with LangGraph and hardening them for production with strict state management, retries/fallbacks, validation layers, and database-backed observability/audit logs, collaborating closely with legal/compliance stakeholders.”

Manas Kumar Sherla

Screened

Junior Machine Learning Engineer specializing in NLP and LLM-based clinical AI

Charleston, IL2y exp

“Built a production automated resume matching system using Python, FAISS vector search, and Selenium-based job scraping, including mitigation for IP blocking and heterogeneous site structures. Also develops LLM/RAG applications with LangChain, using Pydantic-guardrailed structured outputs and LLM-as-a-judge evaluation (including a project focused on tone/semantics for a 3D avatar’s emotional responses).”

Bishara Bishara

Screened

Entry-Level Software Engineer specializing in AI/ML pipelines

1y exp

“Built a production LLM-powered interview-prep app that ingests job postings and generates tailored preparation plans. Iterated from a single generalist LLM to a multi-LLM pipeline and used RAG to ground the final chat assistant on locally stored intermediate outputs; has also experimented with n8n vs Python-coded pipelines for orchestration.”

“Early-career frontend developer who has built an interactive medieval battle game board in React and JavaScript with real-time state synchronization across a 10x10 grid. Shows strong architectural instincts by separating game logic from UI and has also solved responsive UX issues in a farmer marketplace capstone by redesigning dense desktop-first layouts for mobile readability.”

Tanushree Kakad

Screened

Intern Software Engineer specializing in AI, computer vision, and VR

Remote0y exp

“Robotics software engineer with hands-on experience integrating vision-based perception with control logic in ROS simulation environments. Focused on debugging real-time timing/data-flow issues, improving system stability through incremental scenario testing in Gazebo, and supporting reliable deployments with Docker and basic CI/CD automation.”

Kumar Manik

Screened

Intern AI Engineer specializing in LLMs, MLOps, and RAG systems

0y exp

“Built and shipped a production-grade RAG-powered news summarization and Q&A product, tackling real-world issues like retrieval drift, hallucinations, latency, and autoscaling deployment (Docker + FastAPI + Streamlit Cloud). Experienced in end-to-end ML/LLM workflow automation using Airflow, Kubeflow Pipelines, and MLflow, and has demonstrated business impact (40% inference precision improvement) through close collaboration with non-technical stakeholders at Evoastra Ventures.”

“Built and deployed a production RAG chatbot using a vector database + LangChain-orchestrated pipeline, focusing on grounded, context-aware responses. Demonstrates practical trade-off thinking (retrieval quality vs latency/cost), hallucination control, and iterative improvement through logging, manual review, and stakeholder feedback loops.”