Vetted Deep Learning Professionals

“Built and deployed a production NLP sentiment analysis system at Piper Sandler to turn noisy, finance-specific customer feedback into scalable insights. Demonstrates strong end-to-end MLOps: fine-tuning BERT, improving label quality, monitoring for language drift, and automating retraining/deployment with Airflow and Docker (plus Kubeflow exposure).”

“Built a production LLM-powered clinical note summarization and retrieval system that structures patient/provider/payer discussions into standardized outputs (symptoms, treatments, clinical codes, and prior-auth decisions) and stores notes as embeddings for hybrid search and proactive prior-authorization prediction. Experienced with LangChain/LangGraph orchestration, RAG, and grounding against medical code databases, and has communicated model feasibility/limitations to business stakeholders (Virtusa/Comcast).”

“Recent master’s graduate in robotics with applied experience across reinforcement learning and ROS 2 autonomy stacks. Built an RL-based drone vertiport traffic controller (PPO) focused on reward design and simulation integration, and has hands-on navigation work in ROS 2 including LiDAR preprocessing, SLAM/path planning, and stabilizing TurtleBot3 wall-following. Also brings deployment experience containerizing robotics nodes and scaling them with Kubernetes on AWS.”

“Built and deployed a production, multi-tenant modular agentic AI platform at Easybee AI, using LangChain/LangGraph with Redis-backed durable state to make agents reusable, traceable, and auditable. Emphasizes reliability via strict tool schemas, deterministic controllers, tenant-level policy enforcement, and regression testing derived from real production failures; also delivered AI automation for legal/finance workflows (attorney draw and expense automation) with explainable, deterministic payouts.”

“Built and deployed a production LLM-powered demand and churn forecasting system for an e-commerce client, combining open-source LLMs (LLaMA/Mistral) and Sentence-BERT embeddings to generate business-friendly explanations of forecast drivers. Strong focus on data quality and model trust (validation, baselines, segmented monitoring) and production reliability via Airflow-orchestrated pipelines with readiness checks, retries, and ongoing drift/A-B testing.”