Vetted Deep Learning Professionals

“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.”