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Vetted Teaching Assistants in the DMV
Pre-screened and vetted in the DMV.
Senior Corporate Development & Strategy professional specializing in M&A and product strategy
Intern UX/Design Researcher specializing in human-centered design and brand systems
Senior Data Scientist specializing in Generative AI/NLP for legal and healthcare domains
Mid-level Machine Learning Research Engineer specializing in foundation models and GenAI
Pavan Kishore Ramavath
Intern Software Engineer specializing in machine learning and backend systems
“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.”
Mid-level Cybersecurity Consultant specializing in cloud and container security
Mid-level Data Scientist specializing in geospatial computer vision and ML
Amaan Mohammed
Junior AI/ML Engineer specializing in LLM applications and RAG systems
“Built and deployed LLM-powered agentic systems including a multi-agent travel planning assistant using LangChain, RAG (FAISS), real-time APIs, and a supervisor agent to manage coordination and reduce hallucinations. Also developed a Text-to-SQL system with schema-aware validation guardrails, and collaborated with drilling domain experts at CNPC USA to build an ML model predicting rate of penetration (ROP).”
Mid-level AI/Data Scientist specializing in NLP, RAG chatbots, and GenAI on AWS
Abhinav Vengala
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
“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.”
Senior Robotics & Machine Learning Researcher specializing in planning under uncertainty
Mid-level Software Developer specializing in backend microservices and real-time streaming
Mid-level Growth & Finance Analyst specializing in AI startups, public affairs, and real estate finance
Mid-level Robotics Researcher specializing in multi-agent reinforcement learning and game theory
Mid-Level Software Engineer specializing in distributed systems and event-driven microservices
Mid-level Software Engineer specializing in AI and backend systems
Principal XR/Game Engine Researcher specializing in AI-driven VR/AR tools and Unity development
Abhish Khanal
Senior Robotics Researcher specializing in Embodied AI and learning-augmented planning
“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.”
Mid-level Robotics & AI Researcher specializing in learning-augmented task and motion planning
Mid-level Java Software Engineer specializing in backend systems and AI-integrated platforms
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).”