Vetted Latency Optimization Professionals

“Robotics software/embedded engineer who helped build an end-to-end perception sensor platform in a two-person team, owning PCB design, ROS architecture (sensor/processing/diagnostic nodes), and documentation. Experienced integrating heterogeneous sensors over CAN with Arduino and optimizing real-time performance bottlenecks (camera and high-frequency streams) using compression, grayscale pipelines, and reduced inference frequency; also containerized the system with Docker.”

“Built and shipped a production-grade RAG-powered news summarization and Q&A product, tackling real-world issues like retrieval drift, hallucinations, latency, and autoscaling deployment (Docker + FastAPI + Streamlit Cloud). Experienced in end-to-end ML/LLM workflow automation using Airflow, Kubeflow Pipelines, and MLflow, and has demonstrated business impact (40% inference precision improvement) through close collaboration with non-technical stakeholders at Evoastra Ventures.”

“Built and deployed a production RAG chatbot using a vector database + LangChain-orchestrated pipeline, focusing on grounded, context-aware responses. Demonstrates practical trade-off thinking (retrieval quality vs latency/cost), hallucination control, and iterative improvement through logging, manual review, and stakeholder feedback loops.”