Vetted Android Professionals

“CMU IoT coursework project builder who implemented an end-to-end TinyML gesture recognition system on a Particle Photon + ADXL345, streaming data via MQTT/Node-RED to a real-time Node.js frontend and deploying a quantized logistic regression model on-device. Also explored multi-drone coordination, implementing leader-follower offset control and a pivot/arc turning strategy to avoid collisions, and brings practical Docker/Kubernetes plus CI/CD workflow experience from internships.”

“Robotics-adjacent software engineer with hands-on ROS 2 experience building and integrating sensor nodes (IMU, GNSS, wheel encoder) and working with distributed pub/sub concepts via ROS IDL and DDS. Also has Gazebo exposure through Udacity coursework and uses Docker as a core development/deployment tool, with related experience in automotive camera-based solutions.”

“Software engineer with 2.5 years at Amazon working on the Package Tracking page (Amazon’s most visited page), delivering new customer actions like Cancel Delivery and Buy Again within a Spring Boot-based stack. Has hands-on experience with feature-flagged rollouts (Weblab), A/B testing, and scaling/load testing for Prime Day-level traffic, and is upskilling into React while leveraging modern AI coding tools on side projects.”

“Former multi-startup CTO with experience supporting successful capital raises and working directly with boards/investors. Known for implementing operational systems that reduce client escalations (e.g., a status/semaphore overview board) and for building UX-forward MVPs that evolve into automated platforms, including a loan underwriting system for a small US lender.”

“Qualcomm engineer building Android applications that run on Qualcomm AI accelerators, with hands-on experience in C++ concurrency, chipset stress testing, and power/performance tuning. Has deployed on-device AI models and built deployment/log post-processing workflows using Docker/Kubernetes and CI/CD; interested in translating this embedded AI/performance background into robotics (perception/real-time systems).”

“Built a production LLM/RAG assistant for insurance/health claims agents that ingests 100–200 page patient PDFs via OCR (migrated from local Tesseract to Azure Document Intelligence) and delivers grounded claim detail retrieval plus summaries with PII/PHI guardrails. Experienced orchestrating large workflows with Celery worker pipelines and AWS Step Functions (S3-triggered, Fargate-based batch inference/accuracy aggregation), and collaborates closely with non-technical SMEs (claims agents/nurses) through shadowing, iterative demos, and SME-defined evaluation.”