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Hi, I’m Richard Chiou, an AI/ML engineer with 10+ years designing, developing, and deploying production-grade machine learning and deep learning solutions across computer vision, NLP, GenAI, and real-time analytics. I’ve led impactful projects like WildTrack, achieving 95%+ accuracy in species and individual footprint identification, and built a healthcare virtual assistant that summarizes doctor-patient conversations to deliver personalized health guidance. I’m proficient in Python and API-driven cloud deployments (AWS, GCP, Azure), with strong experience building reproducible MLOps pipelines (MLflow, DVC, Docker) and scalable AI platforms. I enjoy collaborating with cross-functional teams to translate data into practical, trusted solutions that improve safety, health, and efficiency, and I’m passionate about advancing AI responsibly.…Hi, I’m Richard Chiou, an AI/ML engineer with 10+ years designing, developing, and deploying production-grade machine learning and deep learning solutions across computer vision, NLP, GenAI, and real-time analytics. I’ve led impactful projects like WildTrack, achieving 95%+ accuracy in species and individual footprint identification, and built a healthcare virtual assistant that summarizes doctor-patient conversations to deliver personalized health guidance. I’m proficient in Python and API-driven cloud deployments (AWS, GCP, Azure), with strong experience building reproducible MLOps pipelines (MLflow, DVC, Docker) and scalable AI platforms. I enjoy collaborating with cross-functional teams to translate data into practical, trusted solutions that improve safety, health, and efficiency, and I’m passionate about advancing AI responsibly.

Richard Chiou

AI Engineer, Data Scientist, AI Developer, +2

5.0 (1 review)

Hi, I’m Richard Chiou, an AI/ML engineer with 10+ years designing, developing, and deploying production-grade machine learning and deep learning solutions across computer vision, NLP, GenAI, and real-time analytics. I’ve led impactful projects like WildTrack, achieving 95%+ accuracy in species and individual footprint identification, and built a healthcare virtual assistant that summarizes doctor-patient conversations to deliver personalized health guidance. I’m proficient in Python and API-driven cloud deployments (AWS, GCP, Azure), with strong experience building reproducible MLOps pipelines (MLflow, DVC, Docker) and scalable AI platforms. I enjoy collaborating with cross-functional teams to translate data into practical, trusted solutions that improve safety, health, and efficiency, and I’m passionate about advancing AI responsibly.…Hi, I’m Richard Chiou, an AI/ML engineer with 10+ years designing, developing, and deploying production-grade machine learning and deep learning solutions across computer vision, NLP, GenAI, and real-time analytics. I’ve led impactful projects like WildTrack, achieving 95%+ accuracy in species and individual footprint identification, and built a healthcare virtual assistant that summarizes doctor-patient conversations to deliver personalized health guidance. I’m proficient in Python and API-driven cloud deployments (AWS, GCP, Azure), with strong experience building reproducible MLOps pipelines (MLflow, DVC, Docker) and scalable AI platforms. I enjoy collaborating with cross-functional teams to translate data into practical, trusted solutions that improve safety, health, and efficiency, and I’m passionate about advancing AI responsibly.

Available to hire

Hi, I’m Richard Chiou, an AI/ML engineer with 10+ years designing, developing, and deploying production-grade machine learning and deep learning solutions across computer vision, NLP, GenAI, and real-time analytics. I’ve led impactful projects like WildTrack, achieving 95%+ accuracy in species and individual footprint identification, and built a healthcare virtual assistant that summarizes doctor-patient conversations to deliver personalized health guidance.

I’m proficient in Python and API-driven cloud deployments (AWS, GCP, Azure), with strong experience building reproducible MLOps pipelines (MLflow, DVC, Docker) and scalable AI platforms. I enjoy collaborating with cross-functional teams to translate data into practical, trusted solutions that improve safety, health, and efficiency, and I’m passionate about advancing AI responsibly.

Skills

Experience Level

Expert

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Expert

Language

English

Fluent

Work Experience

AI/ML Engineer, Data Scientist at Outlier

January 1, 2024 - Present

Fine-tuned computer vision and deep learning models (ResNet-50) using PyTorch to identify species, individual animals, sex, and age-class from wildlife footprint images, achieving over 95% accuracy. Integrated optimized ONNX models into WildTrackAI mobile app, reducing prediction time from 2s to 300ms, enabling 1000+ field users. Deployed a BioBERT-based LLM model using LangChain-powered RAG on SageMaker to handle 500+ patient conversations per month, delivering personalized healthcare advice. Improved clinical workflow efficiency by presummarizing patient information from virtual assistant conversations using a BART-based Transformer, reducing average doctor-patient meeting time by 30%. Led a cross-functional AI team of 7, implementing reproducible MLOps with MLflow, DVC, Docker, and AWS S3, cutting deployment time by 40%.

Senior AI/ML Engineer at StreetLight Data

January 1, 2022 - December 1, 2023

Architected spatio-temporal forecasting models using Graph WaveNet to predict traffic flow one hour ahead, achieving 23% higher accuracy than ARIMA. Applied transformer-based temporal encoders for multi-horizon congestion prediction across heterogeneous inputs, enabling early evacuation bottleneck detection and reducing prediction error by 18%. Created real-time incident detection models combining loop detector data, GPS traces, and DOT feeds to classify crashes and stalled vehicles with 91% precision and 87% recall, reducing operator detection latency by 35%. Built reinforcement learning model for adaptive traffic signal timing and contraflow recommendations, validated in SUMO simulations with up to 18% improvement in throughput during peak evacuation load. Established real-time traffic AI pipelines on AWS, ingesting 500K+ daily GPS and sensor records through Kafka, preprocessing with Spark, and deploying models on SageMaker with 99.9% uptime for high availability.

Data Scientist, Instructor at Metis

August 1, 2019 - December 1, 2021

Mentored learners in feature engineering, model evaluation, and deployment, resulting in measurable skill improvements and career placements for 70+ students in data science roles. Authored 50+ hands-on exercises and guided 20+ capstone projects, producing production-ready ML models and analytics solutions.

AI/ML Engineer at Signifyd

August 1, 2018 - July 1, 2019

Engineered machine-learning models to detect fraudulent e-commerce transactions, processing 100K+ daily; reduced chargebacks by 15%. Optimized adaptive fraud thresholds and conducted A/B tests, improving legitimate transaction approvals by 10% while maintaining high fraud detection accuracy.

Senior Data Scientist at Brain Technologies

July 1, 2017 - July 1, 2018

Formulated data generation guidelines and managed data generation workflows, creating and annotating 50,000+ examples to support the NLP team and ensure high-quality training data. Operationalized general and domain-specific entity recognition systems using spaCy and SENNA, improving the virtual assistant’s entity extraction accuracy by 25%.

Mid-level Data Scientist at xAd

August 1, 2016 - June 1, 2017

Developed a patent-pending hierarchical Bayesian modeling on 10M observations to improve ad campaign-driven store visits by 25%. Built a model ranking mobile user ID quality leveraging 30+ features, increasing targeting precision by 20%.

Mobile Tools Development Intern at PayPal

August 1, 2014 - April 1, 2015

Constructed the PPUtils tool using QIJaws API to automate the creation of 10K+ test user accounts, supporting CI/CD for PayPal iOS and Android apps. Performed root cause analysis on Android test failures and built a login performance dashboard with automated alerts, improving testing efficiency by 45%.