Vetted Research Assistants

“Founding member of Tausight, building AI systems to detect and protect PHI for healthcare organizations; helped take the company through post–Series A funding and exited after ~6 years. Drove a strategic collaboration with Intel’s OpenVINO team—becoming the first to deploy it in a real production system and improving model performance by ~30% on customer Intel-CPU machines.”

“AI/LLM engineer with published work who built FinVet, a production financial misinformation detection system using multi-pipeline RAG, confidence-based voting, and evidence-backed outputs (F1 0.85, +37% vs baseline). Also built NexusForest-MCP, a Dockerized Model Context Protocol server exposing structured global deforestation/carbon data via SQL tools for reliable LLM tool use. Previously delivered borrower risk-rating (PD) models at BMO Financial Group that were validated and integrated into an enterprise credit system through close collaboration with credit officers and portfolio managers.”

“Full-stack engineer and ML assistant in UC Irvine’s CS department who deployed a lab project showcase platform and integrated on-demand execution of computational projects using Docker for isolation. Also built and optimized Linux cloud/cluster test automation for research, diagnosing RAM and network sync bottlenecks, and later led development of a Python-based predictive analytics tool for musicians using probabilistic graphical models and flexible data pipelines.”

“Software engineer who owned an Order Management System end-to-end at Reliance Jio, improving large-table performance via UI virtualization shipped behind feature flags and refined through direct ops-user observation. Also built an OCR automation tool at Piramal Realty using Python/Tesseract with validation and manual correction fallbacks, driving adoption by operations teams. Experienced integrating with Kafka-based microservices and improving observability using structured logging and correlation IDs.”

“QA automation engineer with hands-on ownership of Selenium (C#) and Cypress (JavaScript) suites, including CI integration in GitLab with PR smoke gating and nightly regressions with JUnit reports/screenshots. Drove a reported ~60% reduction in manual effort, improved suite maintainability through reuse/merging tests, and proactively shaped requirements/acceptance criteria in sprint planning to prevent defects (including claims calculation and server/log-related issues).”

“Backend engineer who significantly evolved an internal analytics/reporting platform (Python API + Postgres) powering self-service dashboards for product/business teams, focusing on reliability under heavy concurrent load and fast query performance. Demonstrates strong production engineering practices across API design (FastAPI), observability, incremental rollouts with feature flags, and data security using JWT/RBAC plus Postgres row-level security.”

“Machine Learning Intern at Docsumo who delivered a customer-facing fraud-detection solution end-to-end: rebuilt the pipeline, deployed a Random Forest model, and shipped a Python/Flask microservice on AWS SageMaker. Drove measurable production impact (precision +30%, processing time cut in half, manual review -60%, customer satisfaction +15%) and demonstrated strong customer integration and live-incident response skills.”

“Graduate student researcher on the EARTH project (college collaboration with Moog) working on robotics for an arm/bucket system. Implemented waypoint-based path planning, built an Apriltag data pipeline, and developed ROS 2 tooling including a joystick-to-DeltaCAN teleop node; exploring reinforcement learning policies trained from Tera simulator + ROS 2 bag data to optimize trajectory planning under varying pressure/load conditions.”

“Robotics/AI candidate who built an AI-driven landmark location tool during a summer internship at Mobile Drive, combining YOLOv5 object detection with OpenStreetMap-based geolocation to handle dense, cluttered urban environments. Also researched deploying LLM-based agents on constrained hardware using quantization plus LoRA/continuous learning, improving accuracy from ~80% to ~92%, with an emphasis on production logging for reliability.”

“Built and productionized an AI NL-to-SQL capability inside legacy accounts receivable software (React + Spring Boot + Postgres/pgvector RAG), adding semantic caching and a SELECT-only validation layer to satisfy infosec. Achieved measurable impact (3 days to seconds turnaround, 60% token cost reduction, 50% latency reduction) with strong adoption (40 analysts, 50+ queries/week) and documented/monitored via Confluence + logging and user feedback loops.”

“Full-stack engineer who owned an end-to-end production feature for a Piraeus Bank stock exchange module, spanning React/TypeScript, backend services, and cloud operations with Docker + CI/CD, delivering reported 90% faster API responses and improved uptime. Also built a Smartwound research MVP on AWS, creating a Python image-processing/scoring pipeline to ship despite unclear image-analysis specs.”

“Research Assistant at the University of Houston who built and live-deployed a production RAG system for 1000+ research documents, using hybrid retrieval (dense+BM25+RRF) with cross-encoder reranking and RAGAS-based evaluation; reported 66% MRR, 0.85+ faithfulness, and 68% lower LLM inference costs. Also built a deployed LangGraph multi-agent research system (Researcher/Critic/Writer) with tool integrations (Tavily, arXiv) and dual memory (ChromaDB + Neo4j), plus freelance automation work delivering a WhatsApp chatbot and n8n workflows for a wholesale clothing business.”

“Lead hardware/integration engineer on a high-speed autonomous drone for adversarial drone tracking and surveillance, owning the full stack from custom electronics and flight-controller tuning to ROS2 SLAM and GPU-accelerated computer vision. Built a custom USB-based ROS2 LiDAR driver and migrated SLAM from SLAM Toolbox to Google Cartographer, while boosting YOLOv5 tracking from ~2 FPS to ~45 FPS on an NVIDIA Jetson through CUDA/dependency optimization.”

“Built and evolved a geospatial ETL + API platform that processes pixel-wise satellite imagery in PostgreSQL/PostGIS into low-latency farm-level time-series metrics for an interactive dashboard, using precomputed hotspot analysis to reduce latency by 75–80%. Experienced in FastAPI-style API contract design (OpenAPI), caching, server-side filtering/compression, and production-minded security patterns (RBAC, session-derived authorization, password hashing) with disciplined rollback/versioning practices.”

“Early-career engineer from BNP Paribas who drove a large-scale observability modernization—selecting and implementing Prometheus/Grafana for a 2000+ server estate, then productionizing it on Kubernetes via Docker/Jenkins. Known for hands-on demos, strong documentation/templates, and pragmatic troubleshooting (including custom Python metrics) that improved visibility and cut debugging time by ~60%.”

“Built and deployed AcademiQ Ai, a production LLM-based teaching assistant using GPT/BERT with RAG (LangChain + Pinecone) to handle large student notes and generate adaptive explanations/quizzes. Demonstrated measurable retrieval-quality gains (18% precision improvement, 22% less irrelevant context) by tuning similarity thresholds and chunking based on user satisfaction signals. Also orchestrated terabyte-scale, real-time demand forecasting pipelines using Airflow and Kubeflow on GCP with strong monitoring, shadow deployment, and feedback-loop practices.”

“Integration engineer at Epic focused on healthcare interoperability, including complex radiology integrations (orders/results) spanning more than a dozen downstream systems and PACS vendors. Experienced coordinating clinical Radiant teams, interface analysts, and networking teams to scope workflows, route/transform messages via an interface engine, and troubleshoot intermittent production issues using structured isolation and live testing.”

“Built a production multi-agent LLM system at Live Nation on Databricks (LangGraph/LangChain) that let venue/event teams ask questions in Slack, auto-generated optimized route schedules, and produced inventory/stocking recommendations from historical SQL data and venue trends. Improved reliability by tightening prompts with strict JSON schemas, providing sample questions/SQL, and adding guardrails plus synthetic/edge-case testing, while iterating with event managers and senior VPs via prototypes and feedback loops.”

“Robotics engineer who built an autonomous driving mobile robot software stack at Boston University using ROS on an NVIDIA Jetson Nano, integrating LiDAR + stereo vision with YOLOv5 and a probabilistic occupancy grid for planning. Demonstrated real-time systems rigor (multi-rate ROS nodes, 50ms sync, profiling/instrumentation) and optimized YOLOv5 with TensorRT, citing ~30% accuracy improvement; also taught ROS workshops to 50+ students.”

“NLP/ML practitioner who built an explainable, clinician-aligned system to detect cognitive decline (Alzheimer’s/stroke-related) from audio responses, achieving 97% accuracy on only a few hundred data points. Also has experience with healthcare claims entity resolution and prototyped a word2vec-based patent search vector database in Elasticsearch, with strong emphasis on testing, interpretability, and scalable Python data workflows.”