Aidan Li
@aidanli
Senior AI/ML engineer delivering production-ready LLM, computer vision, and MLOps solutions.
What I'm looking for
I am a Senior AI/ML engineer who builds production-ready large language model, computer vision, and financial ML systems, bridging research and applied engineering to deliver measurable business impact. I have led generative AI, AutoML, and DevSecOps initiatives that improved developer productivity, security resolution, and deployment predictability across enterprise and high-growth startup environments.
My work emphasizes scalable MLOps, model governance, and secure AI deployment—fine-tuning LLMs, designing RAG and embedding pipelines, and deploying models at scale on cloud and Kubernetes platforms. I consistently drive adoption and performance gains via explainable AI, observability, and enterprise compliance practices, enabling faster releases, reduced remediation times, and increased revenue tied to AI-enabled products.
Experience
Work history, roles, and key accomplishments
Led integration of PyTorch transformer models into CI/CD to deliver Code Suggestions and Chat APIs, increasing developer throughput by 25% and cutting time-to-merge by 30%. Architected RAG and LoRA fine-tuning pipelines and AI governance frameworks that reduced MTTR by 40% and improved security resolution rates by 50%.
Engineered Cohere's inaugural LLM API on TPUs and built multilingual embeddings and RAG pipelines, driving an 800% surge in API consumption and cutting per-request latency by 50% while improving semantic accuracy across 100+ languages by 40%.
Built predictive risk scoring and real-time income verification pipelines using XGBoost/LightGBM and PyTorch, reducing ACH return rates by 30% and improving income-source detection accuracy by 25% while cutting onboarding latency by 40%.
Led edge-AI and video understanding initiatives, optimizing CNNs for on-device inference to achieve 4× faster deployments and designing multi-model orchestration workflows that tripled automation throughput and improved video tagging precision by 35%.
Developed AutoML model selection and HPO pipelines using scikit-learn and XGBoost, accelerating prototype delivery 5× and improving time-series forecast accuracy by 20% through advanced feature engineering.
Education
Degrees, certifications, and relevant coursework
Columbia University
Master of Science, Artificial Intelligence
2012 - 2014
Completed a Master of Science in Artificial Intelligence focusing on advanced AI and machine learning methods and research-driven applications.
Stony Brook University (SUNY)
Bachelor of Science, Computer Science
2008 - 2012
Earned a Bachelor of Science in Computer Science with coursework and projects in software engineering, algorithms, and systems.
Tech stack
Software and tools used professionally
Postman
GitHub
GitLab
Kubernetes
GitLab CI
Jupyter
NumPy
Pandas
MySQL
PostgreSQL
MongoDB
Cassandra
Gmail
.NET
Databricks
Neo4j
Figma
OpenCV
Redis
Terraform
Jira
JavaScript
Java
Julia
TensorFlow
PyTorch
MLflow
scikit-learn
Keras
Kubeflow
DataRobot
Kafka
iOS
Elasticsearch
Serverless
Airflow
dockerized
Time Analytics
Vibe
SQL
XGBoost
Hugging Face
LightGBM
Ray
Delta Lake
Availability
Location
Authorized to work in
Job categories
Skills
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