Wenhao He (Jacky)
Ready to Build, Scale, and Innovate
Software Engineer with nearly 2 years of startup experience across full-stack development, AWS-backed systems, and production feature delivery.
About Me
Experience
Developed product features for Clipp, a B2B platform that helps wholesale suppliers and restaurant buyers manage orders, customer interactions, and purchasing workflows.
Built a real-time messaging system using Stream Chat API, supporting 500+ concurrent users across web and mobile platforms.
Expanded frontend and backend test coverage with Jest across ordering, products, promotions, vendors, purchase orders, returns, accounting, and team workflows, achieving 90%+ branch coverage.
Contributed to AI-powered chatbot workflows using Botpress and Python, helping restaurant users place orders for wholesaler products through web and mobile applications.
Led development of a React Native and Expo mobile membership app using JavaScript, Firebase, and Azure, delivering profile, rewards, payments, and job workflows for 300+ members.
Built job board features that allowed users to browse opportunities, view job descriptions, and submit job postings within the app.
Integrated Azure Form Recognizer into the job posting flow to extract content from uploaded job description PDFs and streamline data entry.
Improved mobile profile performance by compressing and resizing uploaded images before storing them in Firebase, reducing load times from minutes to seconds.
class SoftwareEngineer: def __init__(self): self.name = "Wenhao He" self.role = "Software Engineer" self._email = "wenhaohe8@gmail.com" self.education = [ "M.S. in Artificial Intelligence — University at Buffalo (SUNY)", "B.S. in Computer Science — University at Buffalo (SUNY)", ] self.experience = [ "Software Engineer @ Clipp — New York, NY", "Software Engineer @ CAN International — New York, NY", ] self.stack = { "languages": ["Python", "TypeScript", "JavaScript", "C/C++", "C#", "Java", "Go"], "frontend": ["React", "Next.js", "React Native", "Angular", "Tailwind"], "backend": [".NET", "Node.js", "Express", "Flask", "Spring", "GraphQL"], "databases": ["MySQL", "PostgreSQL", "MongoDB", "DynamoDB", "Redis"], "ai_ml": ["LLMs", "RAG", "Transformers", "TensorFlow", "PyTorch"], "cloud": ["AWS", "Terraform", "Docker", "Kubernetes", "GCP", "Azure"], } @property def current_focus(self): return "Building scalable full-stack & AI-powered solutions" def __repr__(self): return f"{self.name} | {self.role}"
Skills
Programming
Developing scalable solutions across low-level systems and high-level scripting.
AI & Machine Learning
Building intelligent systems with LLMs, RAG pipelines, and ML/DL frameworks for NLP and beyond.
Cloud & DevOps
Deploying and managing scalable, containerized applications on cloud platforms with IaC and orchestration.
Frontend & UI
Developing dynamic, high-performance web and mobile applications with modern frameworks.
Backend & Databases
Designing secure, efficient backends with REST APIs, GraphQL, and relational/NoSQL databases.
Deployment, Testing & Tools
Streamlining CI/CD pipelines, testing, and developer workflows.
Projects
ResumeMatch
Serverless AI resume analyzer built on AWS. Designed an event-driven pipeline (S3 → Lambda → Textract → Bedrock) for 4-pass LLM analysis including keyword extraction, match scoring, and resume rewriting. Deployed as a React/TypeScript SPA with Cognito auth, DynamoDB persistence, and CloudFront delivery.
Movie Recommendation System
Built an AI-powered movie recommendation system using Transformer models & TMDb metadata for personalized content. Engineered a scalable ML pipeline with AWS S3 & SageMaker for real-time inference & deployment.
Audio Cloning
Developed a deep learning project to clone a judge's voice, enabling it to narrate the decision from the Brown v. Board of Education civil rights case. Implemented advanced speech synthesis techniques for realistic voice generation.
Monocular Depth Estimation
Implemented encoder-decoder CNN architecture for estimating 3D distances from 2D images. The system can predict depth information from a single image, enabling applications in robotics, autonomous vehicles, and augmented reality.
Fruit/Vegetable Detection
Conducted a Computer Vision project to detect various fruits and vegetables in 2D images. The system can identify 30+ different produce items with high accuracy, supporting applications in retail automation and agricultural technology.
Blog
Technical Blog
Deep dives into Machine Learning fundamentals and System Design patterns. From neural networks to transformers, and from video streaming to ticketing platforms, explore practical insights and architectural solutions.