Applied AI & Machine Learning Engineer
Hi, I’m Andrew, a machine learning engineer with 10 years of experience designing, researching, developing, and deploying machine learning & AI solutions. I am inspired by simple ideas that are well executed.
My career has focused on generating actionable insights and useful data products with machine learning, natural language processing, big data analytics, and visualization. I am a problem solver comfortable working with cloud platforms, deep learning frameworks, ETL pipelines, and distributed computing. I have a passion for telling stories through data and continuously learning.
Working closely with customers to understand their unique challenges, design and implement custom ML + AI solutions, and ensure seamless integration and optimization of production workloads.
Machine Learning Engineer, Cloud Integrations
Hugging Face ∙ Feb 2025 - Present
Technical lead for enterprise cloud partnerships, architecting production-ready ML infrastructure, and building integrations that enable organizations to deploy state-of-the-art AI systems at scale through cloud provider platforms.
Technical Lead for Dell Partnership
- Primary technical point of contact for Dell Enterprise Hub (DEH) development activities
- Led the design and development for Dell Enterprise Hub 2.0, architecting and implementing production LLM inference infrastructure for enterprise customers
- Expanded model catalog support for 12 new models including priority Llama 4 integration, making Dell Enterprise Hub first partner with availability within hours of official release
- Designed and built App Catalog integration featuring OpenWebUI + AnythingLLM with configurable MCP servers for enterprise deployment
- Resolved 31 critical bugs with our LLM inference engines across multiple hardware backends (AMD MI300x, NVIDIA H200, Intel Gaudi 3) and inference frameworks (TGI, vLLM, SGLang)
- Developed the dell-ai CLI/SDK and dell-helm-chart enabling automated enterprise deployments with Docker/Kubernetes
- Actively contributed to the DEH portal Svelte app to accommodate changes and facilitate new feature releases
Technical Lead for Nutanix Partnership
- Led the integration of Text Generation Inference (TGI) for Nutanix’s GPT-in-a-Box solution
- Responsible for building, testing, benchmarking, validating, and optimizing model-specific inference server configurations for new model releases
Machine Learning Engineer, Customer Success
Hugging Face ∙ May 2023 - Feb 2025
Solution architect and technical advisor for enterprise AI implementations, working closely with startup to Fortune 500 customers to design, optimize, and deploy custom ML solutions.
- Serviced $1.5M+ annual revenue across 16+ enterprise customers while achieving 8.88 average personal NPS score
- Led pre-sales technical discussions resulting in $1M+ deal closure through comprehensive solution scoping, technical validation, and customer communication
- Created 6 distinct workshop curricula (LLM Evaluation, LLM Inference, LLM Training/Fine-tuning, RLHF, RAG, Semantic Search) that were delivered 19 times to enterprise teams
Misc Open Source Contributions
- Initiated and build the integration for the first LLM observability tools on the HF Hub: Arize AI’s Phoenix as HF Space & Langfuse as HF Space
- Migrating from OpenAI to Open LLMs Using TGI’s Messages API + blog post
- Built initial LangChain integration for HF chat models
- Created 20+ example notebooks
Project Highlight: Performance Optimization for Patent & IP Startup
- Designed fine-tuning setup for embedding models using synthetic and human curated patent data, achieving 23% accuracy improvement over baseline
- Optimized a bulk embedding solution for 150M+ patent documents with Text Embeddings Inference (TEI) achieving 9x improvement of 2,700 embeddings/second throughput via multi-GPU load balancing
- Architected scalable hybrid semantic search architecture handling 30B+ embeddings with disk-based storage optimization
Project Highlight: Process Automation with LLMs for Healthcare Startup
- Architected LLM solution to automate psychological evaluation report generation and fact-checking pipeline to minimize hallucinations, reducing manual research & writing time from hours to minutes
- Fine-tuned a vision encoder-decoder model (Donut) to transcribe handwritten essays while preserving spelling/grammar errors that improved character error rate (CER) by~4% over leading OCR solutions
Responsible for staying up-to-date on latest trends in ML, identifying research topics based on advances in academia, and leading research projects to demonstrate how these advances can be applied in practice.
Staff Engineer, Machine Learning Research
Cloudera Fast Forward Labs ∙ Dec 2021 - Dec 2022
FF25: Sythetic Data Augmentation & Semantic Segmentation
- Designed and managed a 3 month research cycle that explored the idea of generating synthetic images using CycleGAN to augment an imbalanced dataset as a means of improving classification performance on an underrepresented minority class
- Developed a prototype to detect manufacturing defects using semantic segmentation
- Implemented and trained a Unet model from scratch and experimented with several methods for dealing class imbalance - a common issue for many real-world modeling use cases
FF24: Neutralizing Subjectivity Bias with HuggingFace Transformers
- Designed and led a 6 month research cycle to explore the NLP task of text style transfer (TST) through an applied use case
- Preprocessed unstructured data and trained classification (BERT) and seq-to-seq (BART) models using HuggingFace (HF) Transformers API’s - published models to HF
- Developed, implemented, and benchmarked a set of custom evaluation metrics for quantifying model performance without labeled data
- Built and deployed a Streamlit application to HF spaces that demonstrates how to apply these models to build an intelligent writing assistant
- Wrote a 4 part blog-series and formal research report to document the modeling approach, experiments, evaluation metrics, and ethical considerations
- Presented this work at Open Data Science Conference (ODSC) West 2022
Other Roles & Projects
- Led the technical interviews for 10+ prospective candidates for team recruitment
- Advised our product team, field engineers, customer teams, and ML community