Shane Zhang (张欣耕)

Co-Founder & Lead Engineer | AI/ML Specialist

Summary

AI/ML Engineer with over 5 years of experience building production-grade Agentic RAG systems and LLM-powered applications. Currently co-founding Empath Legal, where I architect AI tools that accelerate legal research for non-technical professionals. Focused on building AI systems that enhance human creativity rather than replace human judgment.

Work Experience

Co-Founder & Lead Engineer

Empath Legal | Jan 2025 - Present

Piscataway, NJ

Building AI-powered legal research tools with my co-founder Grant, focused on creating systems that accelerate lawyers’ analysis without replacing their judgment. Bridging the gap between complex AI capabilities and non-technical professionals who need user-friendly, reliable tools for high-stakes decision-making.

Architected and implemented an advanced Agentic RAG system with Pydantic AI, orchestrating multiple GenAI agents using the ReACT framework for improved content retrieval accuracy in legal document analysis.
Designed and developed a vertically scalable RESTful API backend with Litestar (modern Python framework with superior architectural flexibility compared to FastAPI), utilizing WebSocket and Server-Sent Events for async-reactive real-time LLM integration.
Implemented comprehensive evaluation frameworks to ensure high-quality AI-generated outcomes, reducing hallucination rates and maintaining reliability for legal professionals.
Integrated multiple LLM providers including Google Gemini (for long-context processing), Anthropic Claude (for output quality), OpenAI GPT (for personality and reasoning), and open-source models like Mixtral for cost-efficient solutions.
Collaborated on frontend development using TypeScript, SvelteKit, and shadcn to create highly intuitive, user-friendly interfaces designed specifically for non-technical lawyers, emphasizing ease of use and interactive workflows.
Designed and maintained PostgreSQL database architecture with proper indexing strategies, utilizing SQLAlchemy and Advanced Alchemy ORMs to optimize query performance for high-volume AI operations.
Built proprietary GenAI framework for internal use to ensure high-quality, predictable outputs across different LLM providers and use cases.
Fully containerized the solution using Docker for scalable deployment, implementing comprehensive CI/CD pipelines with GitHub Actions, SonarQube, and PyTest for AsyncIO testing.
Engineered workflow orchestration using Celery for task management and Temporal Workflow for handling complex, reliable multi-step processes.
Implemented Redis for PubSub capabilities and caching, and integrated OpenSearch for observability (metrics and traces) alongside structlog for comprehensive logging.
Designed sophisticated prompts and guardrails to ensure AI assists users without nudging or misleading their opinions, keeping human judgment central to decision-making processes.
Led cross-functional team meetings with non-technical stakeholders to ensure AI development aligns with customer needs and delivers meaningful value to legal professionals.
Emphasized strongly typed Python throughout the codebase to ensure robustness, maintainability, and reliability in production environments.

AI/ML Engineer

Citigroup Inc. | Aug 2024 - Dec 2024

Rutherford, NJ

Developed enterprise-scale RAG systems for financial compliance and document analysis, working with structured data processing pipelines and multi-cloud deployments.

Built structured data processing pipelines with Python for efficient document handling and analysis.
Designed RESTful APIs with FastAPI for real-time data ingestion and NLP processing into PostgreSQL databases.
Deployed solutions at scale with OpenShift and Apache Spark, maintaining robust CI/CD procedures.
Leveraged Agentic RAG systems and Knowledge Graphs (RDF and LPG formats) using LangChain to improve retrieval accuracy in AI-driven compliance analysis.
Implemented evaluation metrics and visualizations to ensure LLM quality and reduce error investigation time.
Utilized OpenAI GPT, Claude, and Google Gemini models, and fine-tuned open-source LLMs to develop customized Agentic RAG systems.
Developed human-centered evaluation frameworks (RLHF) to assess LLM performance in real-world scenarios and ensure alignment with user intents.
Deployed AI-driven RAG systems on AWS using S3, EC2, Glue, Lambda, SageMaker, and Bedrock for data processing and LLM integration.
Developed FastAPI interfaces and gRPC protocols for API integration across Azure and AWS cloud providers.

AI/ML Engineer

Robert Wood Johnson University Hospital | Jan 2023 - Aug 2024

New Brunswick, NJ

Built AI-powered research assistant tools for medical researchers, automating document analysis and improving research workflows for academic teams.

Designed, built, and deployed an Agentic RAG system using Python, JavaScript, SQL, and Chroma vector database to automate parsing and summarization of research documents.
Engineered an advanced RAG system with OpenAI and React framework to reduce onboarding time and improve research efficiency for academic staff.
Collaborated with a cross-functional team of academic researchers, integrating feedback into the NLP system to align the platform with research objectives.
Conducted workshops on ML usage for staff members, driving adoption of ML-enhanced workflows across the organization.
Utilized AWS SageMaker for model fine-tuning and AWS Bedrock for serving models in production environments.

Machine Learning Engineer

Fiskkit Inc. | Jan 2020 - July 2021

San Francisco, CA

Integrated NLP-driven features into a production web platform, focusing on real-time text generation and semantic analysis using PyTorch and graph databases.

Integrated NLP-driven features into the Node.js backend with PyTorch (C++ CUDA) for real-time text generation and summarization.
Conducted data pre-processing and exploration using PySpark, NumPy, and Pandas to ensure high-quality data integration for model training.
Optimized deep learning models through quantization and pruning techniques with TensorRT.
Built a semantic graph database using Neo4j and Cypher queries to store and query complex relationships.

Education

M.S. in Computer Science, Machine Learning Specialty
Rutgers, The State University of New Jersey
Dec 2020 - Dec 2022
GPA: 3.71
B.S. in Computer Science
Rutgers, The State University of New Jersey
Sep 2017 - Sep 2020

Certifications

AWS Certified Machine Learning - Specialty (MLS-C01)
Amazon Web Services
Jun 2024
Validates expertise in building, training, tuning, and deploying ML, Deep Learning, Generative AI, and LLM models on AWS
PADI Rescue Diver
Professional Association of Diving Instructors
Dec 2018
Advanced scuba diving certification focused on diver safety and emergency response. Trained in managing stress and panicked divers, with emphasis on remaining calm under life-threatening conditions to assess situations and execute effective rescue solutions.

Teaching

Teaching Assistant
Rutgers University
Dec 2020 - Dec 2022
Tutored students in Computer Science and Machine Learning, helping bridge theoretical concepts with practical implementation

Publications

Berns, M. P., Nunez, G. M., Zhang, X., et al. (Sep 2024). Auditory Decision-making Deficits After Permanent Noise-induced Hearing Loss.