Vetted Teaching Assistants in Virginia

Pavan Kishore Ramavath

Screened

Intern Software Engineer specializing in machine learning and backend systems

Leesburg, VA1y exp

“Built an AI-powered medical coding system at Clinpex that mapped 88,000+ clinical terms to standardized codes, achieving about 86% accuracy and cutting manual review time by over 80%. Brings hands-on backend ownership in a healthcare AI setting, with experience using semantic retrieval, LLM validation, and human review to handle ambiguity and reliability in a regulated domain.”

“Software engineer with experience across data engineering and backend/platform work: owned a Databricks/PySpark real-time pipeline powering customer dashboards with a 15-minute SLA, and helped modernize an investor web app from JSP to React/TypeScript with API + SQL/materialized-view performance improvements. Also contributed to breaking a Java monolith into microservices (Redis + gRPC on AWS EKS) and built an EC2-deployed Play Store/App Store crawler that reduced third-party data costs.”

“LLM/agentic systems engineer who built a production "Agentic AI Diagnostic Assistant" for network engineers, using a multi-agent Llama 2 + LangChain architecture with RAG over telemetry/incident data in DynamoDB and confidence-based deferrals to reduce hallucinations. Also has strong MLOps/orchestration experience (Airflow, EventBridge, Spark, Docker, SageMaker/ECS) at multi-terabyte/day scale and delivered multilingual NLP analytics (fine-tuned BERT/spaCy) for support operations through hands-on stakeholder workshops.”

“Robotics software engineer with experience spanning safety-critical embedded medical hardware (low-cost neonatal baby warmer with PID temperature regulation) and advanced multi-robot planning research (belief-space planning with abstraction + MCTS to handle uncertainty). Strong ROS/ROS2 practitioner (Nav2/SLAM Toolbox/MoveIt) who builds custom packages (e.g., Insta360 panoramic imaging) and is hands-on debugging real robots from SLAM/frontier exploration to multi-robot collision avoidance and real-time performance.”

“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).”