Senior AI & ML Engineer

Jerry Yang

Cell: 254-***-**** Austin, TX

LinkedIn: www.linkedin.com/in/jerry-yang919 ***.*********@*****.*** SUMMARY

8+ years of experience as a Full-Stack & AI/ML Engineer building scalable, cloud-native software and implementing state-of-the-art LLM, GenAI, and NLP systems.

Documented experience working on cross-functional teams and building microservices, CI/CD workflows, and real-time data systems using Python, Java, Kubernetes, and Terraform.

Broad experience integrating RAG architecture, LangChain, Hugging Face, GPT-3.5/4, Gemini, and optimizing end-user experience.

Interested in building end-to-end AI platforms using Python frameworks, deep learning, and robust DevOps and corporate applications.

SKILLS

Languages: Python, Java, Javascript, R, Matlab, Shell Scripting, Sql

AI / ML / Data Science: Machine Learning, Deep Learning, NLP, Google Gemini, Vertex AI, Microsoft 365 Copilot, TensorFlow, Keras, PyTorch, scikit-learn, Transformers, Bert, Lstm, Graph Attention Networks (Gat), Artificial Neural Networks (Ann), Generative Adversarial Networks (GANs), Statistical Analysis, Anomaly Detection, Recommendation Systems, Data Analytics, Neuroimaging Analysis

Frameworks & Libraries: FastAPI, Flask, Django, Java Spring, React, Angular, TensorFlow Serving, Pandas, Numpy

Cloud & Devops: AWS (S3, EC2, SageMaker, Lambda, Glue), Microsoft 365, Azure (Docker Containers), Google Cloud (Vertex AI, Kubeflow, BigQuery), Elasticsearch / Opensearch, Hadoop, Spark, Hive, Docker, Mlops, Devops

Data & Storage: Postgresql, TiDB, Snowflake, Elasticsearch, OpenSearch, ETL Pipelines, Graph Databases

Messaging / APIs & Integration: REST APIs, Gemini API, Microsoft Graph API, SMTP Automation, NATS Jetstream, SeaweedFS (S3-compatible), GCP API Integrations

Testing & Automation: Pytest, Selenium, Unit Testing, Integration Testing, CI/CD Pipelines, Trivy Scanning

Architecture & Methodologies: Multi-tenant Systems, Event-driven Architecture, Microservices, Async I/o, Streaming Pipelines, Scheduler/worker Systems, Agile, Scrum, MLOps PROFESSIONAL EXPERIENCE

Backend & AI Engineer

Microsoft Austin, TX

Jan 2022 – Present

Designed and implemented the Dreaming Worker pipeline to analyze uploaded user content (audio, PDF, markdown, DOCX) and generate structured “Moments” and “Dreams” via GPT-4-class LLMs.

Built a multi-tenant content ingestion system with SeaweedFS (S3-compatible), NATS JetStream, and PostgreSQL/TiDB to manage large-scale asynchronous processing.

Developed file upload and moment creation APIs using FastAPI (Python) integrating LLM agents for automated insight extraction.

Implemented CLI orchestration commands to run per-tenant or batch analysis, including model selection, lookback windows, and email digest generation.

Created automated email reporting via templated HTML summaries of generated moments using SMTP server.

Developed RAG-style pipelines for chunking, embedding, and retrieval of multi-format content (PDF, DOCX, Markdown, audio).

Developed agentic AI systems that interface with OpenAI, Anthropic, and Claude APIs to process user content and generate structured insights.

Integrated OpenAI/Claude embeddings using Python and PostgreSQL or TiDB vector indexes to enable high- performance similarity search.

Integrated Pydantic validation models to enforce structured LLM outputs and ensure schema compliance across the AI insight pipeline.

Enhanced developer experience with async I/O, structured logging, and streaming agent responses for real- time monitoring of content analysis.

Built a secure, event-driven file pipeline using SeaweedFS S3 and NATS JetStream, supporting S3/local storage, automated embeddings, and LLM-powered summarization for intelligent indexing.

Created scheduler and worker systems to automate daily content summarization and insights generation per tenant using NATS JetStream.

Enhanced CI/CD security with Trivy scans, Pytest automation, and coverage reporting in GitHub Actions.

Implemented automated unit testing (Pytest), integration testing, and end-to-end UI testing (Selenium), maintaining test quality and preventing regressions across key booking workflows.

Explored integration patterns for Microsoft 365 Copilot to extend AI capabilities into enterprise productivity workflows.

Backend & AI Engineer

Silicon Labs Austin, TX

Jul 2020 - Jan 2022

Led cross-functional teams leveraging ML, Gen AI, and NLP for financial data analysis and AI-powered investment insights.

Implemented backtesting and generative AI chat assistant for user education and informed decision- making.

Led the development of a legal chatbot powered by LLMs, enhanced with agentic AI technology, and deployed on GCP using Kubeflow and BigQuery.

Demonstrated expertise in cloud-based NLP solutions (Google Cloud, AWS).

Created interactive dashboards for financial data communication using Tableau.

Implemented cutting-edge research in generative AI (LangChain, ChatGPT, Ollama.ai).

Scaled distributed backend systems supporting 20,000 daily active users using FastAPI and Node.js, improving request latency and throughput by 20-40% via API refactoring, advanced caching strategies, and database query optimization (MySQL, RocksDB, resto).

Developed and maintained enterprise-grade microservices in Python and Django for low-latency, high- availability systems.

Built and maintained responsive dashboards and client portals using React and Angular for real-time data visualization and reporting.

Developed cloud-native applications in Azure and Linux-based environments, optimizing deployments and observability with Prometheus and Grafana.

Worked in Agile and Scrum environments, leading sprint planning, daily stand-ups, retrospectives, and backlog grooming.

Mentored four junior engineers, led architecture reviews, and established best practices for scalable distributed systems.

Designed and developed a crypto arbitrage bot using ML algorithms (Python, TensorFlow, GCP).

Developed automated trading strategies and real-time decision-making for high-speed execution in volatile markets.

Machine Learning Engineer

Samsung Electronics, Austin, TX

Jun 2019 - Aug 2019

Provided services for a director in the creation of data pipelines and ML engineering.

Worked on machine learning, NLP, AWS, Elasticsearch/OpenSearch and helped create ETL for Global Hydrogen Index with Elasticsearch.

Utilized BERT for NLP-based deep learning in voice recognition and integrated agentic AI with graph attention networks, transformers, and LSTM architectures.

Streamlined data analytics and workflow automation using Alteryx.

Designed and implemented complex workflows integrating data from various sources, transforming and cleaning data, and generating actionable insights.

Reduced processing time and improved data accuracy, resulting in more informed decision-making.

Participated in the design, development, and deployment of scalable backend services and distributed systems supporting high-traffic e-commerce and financial platforms.

Built and maintained RESTful APIs and microservices using Python for millions of daily users.

Combined DNNs with SVM, KNN, and tree models using grid search, increasing performance by 15%.

Architected and optimized cloud infrastructure on AWS (EC2, Lambda, S3, RDS, DynamoDB) with Terraform and CloudFormation.

Developed and maintained frontend applications using React and Vite.

Implemented CI/CD pipelines with GitHub Actions and AWS CodePipeline.

Integrated AI/ML features using Amazon SageMaker, OpenAI APIs, and Hugging Face models.

Monitored and improved application reliability using Prometheus, Grafana, and CloudWatch. Full Stack Engineer

AMD, Austin, TX

Jan 2019 - Apr 2019

Implemented machine learning in a distributed containerized fashion using TensorFlow, Keras, Azure Docker containers, and scikit-learn within a Jira Agile methodology system.

Built generative adversarial models for anomaly detection and utilized R for statistical analysis and data visualization in helicopter health data projects at Bell Flight.

Built anomaly detection models including vanilla artificial neural networks, Gaussian mixture models, and autoencoders.

Leveraged Alteryx and agentic AI to optimize data processes, achieving significant time and resource savings.

Developed automated, AI-driven workflows that reduced manual data entry by 15 hours per week.

Automated hyperparameter testing with itertools to test hundreds of models in one pipeline.

Built responsive frontends using AngularJS, React, HTML5, CSS3, and JavaScript/TypeScript.

Developed backend services and RESTful APIs using Python and Node.js, integrating with SQL Server and Azure-based services.

Implemented Azure solutions including App Services, Azure SQL, Blob Storage, and Azure Active Directory.

Collaborated in agile teams with UX designers, product managers, and QA engineers to deliver high-priority features on schedule.

Backend Engineer

Hewlett Packard Enterprise, Houston, TX

Jun 2018 - Aug 2018

Built and maintained scalable backend services in Java and Python, integrating with VMware’s virtualization stack and APIs (vSphere, ESXi, vCenter).

Developed and enhanced RESTful and gRPC APIs to support automation, orchestration, and third-party integrations.

Wrote scripts in Python and Bash to automate data collection, parsing, and storage.

Supported integration of backend data pipelines with MySQL and NoSQL databases for large-scale data indexing.

Optimized scripts to automate ETL processes.

Assisted in debugging and performance tuning for distributed systems handling high-volume web data.

Collaborated with senior engineers to improve system reliability and scalability for production workloads. PROJECTS

Dreaming Worker (Microsoft)

An internal Microsoft tool that analyzes user-uploaded files (like audio, PDFs, markdown) and generates structured insights — referred to as “Dreams” and “Moments” — using GPT-4.

Designed and built the backend pipeline using SeaweedFS, NATS JetStream, and FastAPI.

Integrated OpenAI models to summarize diverse content formats.

Added real-time streaming and async handling to improve user experience.

Helped users better understand and engage with their own content. CounselGPT (Silicon Labs)

A legal and finance-focused AI assistant developed at Silicon Labs, used to answer questions based on uploaded documents in a conversational way.Designed and built the backend pipeline using SeaweedFS, NATS JetStream, and FastAPI.

Built the backend LLM pipeline using LangChain and Claude.

Integrated RAG-style search for document-based Q&A.

Deployed using Kubeflow and BigQuery on GCP.

Improved internal accessibility to legal and financial resources. ArbEngine (Personal side project)

A crypto arbitrage bot built as a personal side project to explore automated trading.

Monitored multiple exchanges for pricing gaps and executed trades automatically.

Used TensorFlow models to test and refine trading strategies.

Built a backtesting system to evaluate performance on historical data.

Provided hands-on experience with real-time decision-making systems. H2Index (Samsung Electronics)

A document indexing system developed at Samsung to help analysts quickly find insights from hydrogen market reports.

Used BERT embeddings and Elasticsearch to extract and index relevant information.

Built an AWS Lambda-based ingestion system for automating document processing.

Helped analysts access and search thousands of scientific reports more efficiently. SkyScan (AMD)

An anomaly detection system created at AMD for helicopter telemetry data used in flight safety.

Trained GANs and autoencoders to detect unusual sensor readings

Built a preprocessing workflow using Alteryx and Python

Reduced manual inspection time and enabled early issue detection in flight systems EDUCATION

Bachelor of Science (B.S.), Computer Engineering – The University of Texas at Austin, 2018 CERTIFICATIONS

AWS, AWS Certified Machine Learning - Specialty

TensorFlow, TensorFlow Developer Certificate

COURSES

DeepLearning.AI, LangChain for LLM Application Development

Coursera / Google Cloud, MLOps Specialization

Hugging Face, Transformers Course

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