Vetted Object Detection Professionals

“University Rover Competition autonomous-systems lead who architects and debugs a full ROS 2 autonomy stack (Nav2, vSLAM, EKF fusion) and backs it with strong engineering hygiene (Docker + GitHub Actions CI running headless Gazebo and colcon tests). Also has industry-facing ROS 2 hardware integration experience, building a ros2_control plugin for a Unitree G1 arm using CycloneDDS and optimizing real-time behavior via QoS tuning.”

“Built and deployed a production LLM-powered support assistant for customer support agents using a RAG architecture over internal docs and past tickets, with human-in-the-loop review. Demonstrates strong applied LLM engineering focused on real-world constraints (hallucinations, latency, cost) using routing to smaller models, reranking, caching, and rigorous evaluation/monitoring (offline eval sets, A/B tests, KPI tracking).”

“Built and deployed a production RAG system for financial/compliance teams using GPT-4, Claude, and local models to retrieve and summarize thousands of internal documents with strong security controls (role-based retrieval, PII masking). Drove significant operational gains (30+ hours/week saved, ~35% productivity lift, ~45% faster responses) and orchestrated end-to-end ingestion/embedding/index refresh pipelines with Airflow, S3, and SageMaker while partnering closely with compliance stakeholders on auditability and traceability.”

“Robotics/embodied-AI practitioner who built a TurtleBot3 LiDAR-fingerprint localization pipeline end-to-end (autonomous data collection + multi-head NN) achieving ~30 cm error in a 10x10 m space. Also has industry experience at Infineon building large-scale production data/AI pipelines and rapidly fixing a deployed recommendation system by correcting upstream data normalization, improving accuracy by 20%+.”

“Built and deployed Clyra.AI, an AI-driven daily scheduling product that uses a LangGraph-based multi-agent LLM pipeline (task extraction, verification, reflection) grounded with strict RAG over emails/documents/calendars and real-world signals like health metrics. Designed a custom agent orchestrator with bounded loops/termination conditions and a self-auditing verification/reflection layer to reduce hallucinations while controlling latency and cost via caching and model distillation.”

“Built and deployed a production RAG-based enterprise document intelligence platform for financial/compliance/operational documents on AWS (Spark/Glue ingestion, embeddings + vector DB, LangChain orchestration, REST APIs on Docker/Kubernetes). Deep hands-on experience orchestrating multi-step and multi-agent LLM workflows (LangChain, LangGraph, CrewAI) with strong focus on grounding, evaluation, observability, and cost/latency optimization, and has partnered closely with non-technical finance/compliance teams to drive adoption.”

“Graduate Assistant Intern at Friends University who built and deployed a GenAI-driven requirement understanding system that automates extraction and semantic grouping of technical requirements from large unstructured documents. Demonstrates strong LLM engineering rigor (golden datasets, regression testing, post-processing validation) and production-minded delivery using LangChain/LlamaIndex orchestration, FastAPI microservices, Docker, and cloud deployment.”

“Built and deployed a production LLM/RAG system at CVS to automate clinical documents, addressing PHI compliance, retrieval accuracy, and latency; achieved a 35–40% reduction in review effort through chunking and FP16/INT8 optimization. Also has experience translating AI outputs into actionable insights for non-technical stakeholders (sports analysts).”

“At Delta Airlines, built and shipped a production LLM-powered semantic search/troubleshooting assistant over maintenance logs and operational documentation using OpenAI embeddings and a vector database. Implemented hybrid ranking, query enrichment, and structured filters to improve relevance ~35% while optimizing latency via caching and vector tuning. Also designed a scalable Kafka + AWS (Lambda/SQS) ingestion pipeline with strong reliability/observability and an eval loop using real engineer queries and human review.”

“Robotics software/vision engineer with hands-on experience building motion-tracking systems that fuse camera-based 3D tracking with IMU orientation to reproduce tool motion for automated spray painting. Has implemented ROS nodes/packages for Orbbec camera streaming and SAM3-based segmentation, plus CAN bus coordination between robots and Dockerized deployment for a pick-and-place robotic cell.”

“Robotics/ML candidate building an individual pedestrian trajectory forecasting system by adapting a GAN-style Social-GN training architecture from LSTM to a transformer-based AgentFormer design. Also has hands-on embedded robotics experience debugging lane-following behavior on a JetBot by tuning PID control, and uses Docker for reproducible training environments.”

“Robotics software engineer who led the full perception/manipulation/planning stack for an autonomous watermelon-harvesting robot, including ripe-vs-unripe instance segmentation deployed on Jetson AGX Orin with TensorRT and quantization. Deep ROS 2 experience (custom ZEDx mask driver, LiDAR+stereo fusion, MoveIt 2/Nav2/ros2_control) and proven real-time optimization—cut latency ~40% and achieved consistent 7-second pick cycles in outdoor field conditions.”

“Robotics/Perception Software Engineer at Berkshire Grey who built and hardened a production ROS-based perception + supervision stack for autonomous trailer-unloading robots (RGB-D + LiDAR), including grasp/geometry estimation and segmentation. Diagnosed real-time behavior issues by instrumenting ROS pipelines, then implemented runtime RANSAC-based compensation for LiDAR yaw bias and TF-window validation; also supports containerized deployment on Kubernetes and is actively porting the system from ROS1 to ROS2.”