Vetted Semantic Search Professionals

“Robotics Support Engineer at HAI Robotics supporting a 385-robot warehouse fleet at a Shein client site. Built a production automation and reporting workflow to diagnose and resolve abnormal shelf locations, cutting incidents from ~250/day to ~25/day while providing actionable root-cause data to client/ops/maintenance. Hands-on ROS 2 (Humble) debugging across Nav2/localization/TF and sensor integration issues including QoS and firmware coordination.”

“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).”

“ML/NLP engineer focused on large-scale data linking for e-commerce-style catalogs and customer records, combining transformer embeddings (BERT/Sentence-BERT), NER, and FAISS-based vector search. Has delivered measurable lifts (e.g., +30% matching accuracy, Precision@10 62%→84%) and built production-grade, scalable pipelines in Airflow/PySpark with strong data quality and schema-drift handling.”

“Full-stack engineer who built a live-streaming edtech platform at KratosIQ, owning the entire frontend and the backend streaming layer. Notably migrated the system from a P2P mesh to an SFU architecture to handle scaling under heavy load, and delivered measurable React performance gains (450ms to 40ms render time) validated via Lighthouse and web vitals.”

“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.”