Vetted Model Evaluation Professionals

“Built a production LLM fine-tuning pipeline for domain-specific code generation at Pigeonbyte Technologies, including automated collection and rigorous quality filtering of 10M+ code samples (AST validation, sandbox execution/testing, deduplication, drift monitoring, and human-in-the-loop review). Also implemented end-to-end ML orchestration in Apache Airflow with data quality gates, dataset versioning in S3, benchmarking, and automated model promotion, and has a reliability-first approach to agent/workflow design.”

“Built and productionized an LLM-based system that summarizes large volumes of unstructured content (customer feedback/internal docs) to reduce manual analysis and surface decision-ready insights. Brings strong reliability practices—prompt/schema constraints, validation checks, orchestration with Airflow/Databricks, and rigorous component + end-to-end testing—plus experience partnering closely with business stakeholders to drive adoption.”

“AI/ML practitioner with production experience building a RAG-powered contextual customer support agent, optimizing for low latency using vector databases and smaller LLMs. Also deployed a fraud detection model on Kubernetes with auto-scaling for heavy transactional loads, and improved chatbot accuracy by 15% through metric-driven testing and evaluation. Partners with Marketing on personalization/recommendation initiatives with measurable outcomes tied to customer feedback.”

“Robotics software learner building a self-directed recycling robot project in Isaac Sim, integrating ROS2 + Nav2 + SLAM with camera/LiDAR sensing and CV-based object detection. Has prior hands-on ROS2 work creating a YOLO detection node visualized in RViz and has built/optimized simulated line-follower and maze-solver robots in Webots, documenting progress publicly on GitHub and LinkedIn.”

“Hands-on LLM/agentic workflow practitioner who focuses on moving prototypes into production by improving data quality, preprocessing, and validation, and by testing against real user inputs. Experienced troubleshooting LLM issues in real time during live tests and supporting adoption through developer demos and sales-partnered technical explanations that address customer reliability concerns.”