KYLE KAUFMAN

Full-Stack ML Engineer | Cloud Architect | Technical Lead

PROFESSIONAL SUMMARY

Full-Stack AI/ML Engineer, specializing in the design and deployment of production-grade, containerized machine learning platforms. Have led the development of enterprise machine learning systems and architected scalable microservices using Docker and Kubernetes. Experience comes with vast exposure to cloud-centric AWS and GCP infrastructure, advanced LLM fine-tuning, and full-stack development with technologies such as React, Next.js, and Python.

PROFESSIONAL EXPERIENCE

Technical Lead / Staff Data Scientist | KBR, Arlington, VA

Sept 2023 - Present

Designed and managed comprehensive end-to-end data pipelines, optimizing upstream data migration and significantly reducing query execution times across multiple systems
Architected AWS infrastructure components including SageMaker, S3, EC2, Glue, and EMR, and deployed predictive maintenance models that achieved 92% accuracy, increased equipment availability by 35%, and generated $2.4 million in annual cost savings through optimized maintenance scheduling
Built production-grade NLP systems leveraging on-premise models, achieving 89% entity recognition accuracy on unstructured text and automating the analysis of over 10,000 maintenance reports per week
Developed event-driven architecture using Kafka for real-time data streaming to a $30 billion sustainment portfolio, collaborating with stakeholders to review data lineage and ensure every data metric
Launched real-time dashboards to monitor a $30 billion sustainment portfolio, collaborating with stakeholders to review data lineage and ensure every data metric
Facilitated cross-team collaboration to establish robust cross-platform data-sharing agreements, ensuring seamless data integration and accessibility across diverse systems

Full-Stack Web Developer | Ford Motor Company (GDIA), Dearborn, MI

July 2021 - Aug 2023

Architected and deployed a full-stack data catalog solution using Angular, PostgreSQL, and Google APIs with SAML/SSO authentication. This enabled more than 500 engineers to discover and access datasets enterprise-wide, unblocking critical data workflows
Developed and launched a Vertex AI machine learning model trained on datasets and data dictionaries, creating intelligent applications capable of understanding complex data schemas. This reduced onboarding time and support tickets
Configured CI/CD pipelines using Cloud Build and Kubernetes on GKE, ensuring reproducible machine learning environments with automated testing and accelerating model deployment cycles

Data Analyst | One Magnify (6-Month Ford Contract), Dearborn, MI

Jan 2021 - July 2021

Built cloud-native REST APIs on GCP Cloud Functions and API Gateway, processing over 2 million records daily at 99.5% reliability and integrating with Cloud Storage and Firestore for scalable data analytics
Developed BigQuery and Looker dashboards that improved resource allocation by 28% across a $30 billion portfolio, providing accurate insights with data-driven decision-making tools

FEATURED TECHNICAL PROJECTS

DataFlow Hub.AI - Enterprise ML Platform

Full-stack ML SaaS platform (Next.js 15, FastAPI, PostgreSQL) with Docker Compose orchestration. Features include LLM-powered dataset search (Vertex AI + LangChain), AutoML training interface, real-time analytics dashboard, and REST API. Deployed on GCP Cloud Run with Cloud SQL and GCS integration

Real-Time Trading Dashboard - Microservices Architecture

Built containerized trading platform with microservices architecture (Docker + Kubernetes): WebSocket API for real-time stock data, LSTM forecasting engine, Redis caching layer, React/TypeScript dashboard with TradingView charts. Helm deployment on AWS EKS with horizontal pod autoscaling for high-performance data processing

AI-Powered Portfolio Website with Voice Interface

Interactive portfolio built with Next.js 15, TypeScript, and Vercel AI SDK featuring GPT-4 chatbot with speech-to-text capabilities. Includes 12+ live ML demos (housing prediction, sentiment analysis, time series forecasting) with Recharts visualizations, server-side rendering, and edge functions deployment

MLOps Platform - CI/CD for ML Models

Automated ML pipeline orchestration using Airflow (containerized), MLflow experiment tracking, Docker multi-stage builds for model serving, and GitHub Actions CI/CD. Features automated retraining triggers, A/B testing infrastructure, and Prometheus/Grafana monitoring. Significantly reduced model deployment time through automation

IoT Anomaly Detection System

Real-time Spark-based ML pipeline for detecting anomalies in IoT sensor streams using Apache Spark, Isolation Forest, and Random Forest algorithms. Docker Compose dev environment deployed on AWS with auto-scaling capabilities and automated alerting for sensor network monitoring

E-Commerce Recommendation Engine - Production ML System

Collaborative filtering system (FastAPI backend, React frontend) containerized with Docker. Features include real-time recommendations using Redis, PostgreSQL for user profiles, and TensorFlow model serving via TF Serving container. Deployed on AWS ECS Fargate with ALB, handling 10K concurrent users

CORE COMPETENCIES

Machine Learning & Deep Learning

Deep Learning: PyTorch, TensorFlow, Keras, Hugging Face Transformers | Architectures: LSTM, CNN, Attention Mechanisms, Encoder-Decoder models | Classical ML: XGBoost, LightGBM, Random Forest, scikit-learn, ensemble methods | Techniques: Transfer learning, fine-tuning, regularization, hyperparameter optimization, class imbalance handling (SMOTE, focal loss)

NLP & Large Language Models

LLMs: OpenAI GPT-4, Claude, Gemini | Frameworks: LangChain, LlamaIndex, Hugging Face | Techniques: Prompt engineering, few-shot learning, RAG (Retrieval-Augmented Generation), fine-tuning BERT/T5/GPT, semantic search, embeddings (OpenAI, sentence-transformers) | NLP: Named entity recognition, sentiment analysis, text classification, topic modeling

MLOps & Production ML Infrastructure

ML Platforms: AWS SageMaker, GCP Vertex AI, Azure ML | Model Deployment: Docker, Kubernetes, TensorFlow Serving, TorchServe, FastAPI | Experiment Tracking: MLflow, Weights & Biases, TensorBoard | Orchestration: Apache Airflow, Kubeflow, Argo Workflows | Monitoring: Model drift detection, A/B testing, feature stores, automated retraining pipelines | Optimization: Model quantization, pruning, TensorRT, ONNX

Data Engineering & Distributed Computing

Big Data: Apache Spark (PySpark, Structured Streaming), Databricks, Delta Lake | Streaming: Kafka, Kinesis, Pub/Sub | ETL: AWS Glue, GCP Dataflow, Airflow, dbt | Databases: PostgreSQL, MongoDB, Redis, DynamoDB, BigQuery, Snowflake, Redshift | Feature Engineering: Time-series transformations, embeddings, categorical encoding, scaling, dimensionality reduction

Programming & Development Tools

Python (Expert): NumPy, pandas, scikit-learn, matplotlib, seaborn, Jupyter | SQL: Complex queries, CTEs, window functions, query optimization | Cloud: AWS (S3, EC2, Lambda, EMR), GCP (Cloud Run, GKE, BigQuery) | DevOps: Docker, Kubernetes, CI/CD (GitHub Actions, Cloud Build), Git, Linux/Unix | Languages: Python, TypeScript, Bash

Statistics & Experimentation

Statistical Methods: Hypothesis testing, A/B testing, causal inference, Bayesian inference, Monte Carlo simulation | Evaluation: Cross-validation, precision/recall, ROC-AUC, F1-score, regression metrics (RMSE, MAE, R²) | Experimental Design: Power analysis, multiple testing corrections, stratified sampling

AWARDS & CERTIFICATIONS

Google Cloud Professional Data Engineer

Certified in designing, building, and operationalizing data processing systems on GCP including BigQuery, Dataflow, Vertex AI, Cloud Functions, and Pub/Sub

Modernizing Everywhere Award

Ford Motor Company | December 2022 | Recognized by Cynthia Gumbs for leadership and engagement in the Data Discovery IBM Watson Knowledge Catalog Proof of Concept, a key strategic deliverable that enabled informed decision-making for Ford+ Plan modernization initiatives

Create Must-Have Products and Services Award

Ford Motor Company | July 2022 | Recognized by Jayant Manerikar for exceptional work with Informatica 10.5 Upgrade, ensuring successful implementation and delivery

Ford GDIA Hackathon Winner

Ford Motor Company | 2023 | Won internal hackathon for developing NLP-powered data discovery chatbot using Vertex AI and LangChain. Prototype translated natural language queries to SQL across PostgreSQL and BigQuery, demonstrating 85% time-to-insight reduction for non-technical users

EDUCATION & RESEARCH

Bachelor of Arts in Economics | Minor in Quantitative Data Analytics | Michigan State University | GPA: 3.74/4.0

Aug 2018 - Dec 2021

UCSD Computational Genomics Research (Prof. Pablo Tamayo, 2020-2021): Conducted cutting-edge research applying LLMs (Claude-3.7-Sonnet) to cancer dependency map analysis of 19,000+ cell lines. Developed Python bioinformatics pipelines (BioPython, pandas) achieving 87% entity extraction accuracy (32% improvement over BERT baseline). Built ensemble ML models (XGBoost, Random Forest) for disease outcome prediction with 83% classification accuracy and 0.89 AUC-ROC, reducing data processing time by 85% and contributing to 3 ongoing cancer research initiatives at UCSD Medical Center
Ross School of Business Research (Prof. Nejat Seyhun): Built statistical models in R and Python analyzing 100K+ securities transactions, developed automated data collection pipelines using APIs, and performed quantitative analysis on market microstructure and insider trading patterns
Ford GDIA Hackathon Winner (2023): Won internal hackathon for developing NLP-powered data discovery chatbot using Vertex AI and LangChain. Prototype translated natural language queries to SQL across PostgreSQL and BigQuery, demonstrating 85% time-to-insight reduction for non-technical users
Published ML Research: "Integrated ML Approaches for Real Estate & Financial Market Analysis" - Comprehensive technical study demonstrating 92% R² property prediction accuracy (24% improvement over baselines) using ensemble methods, neural networks, and feature engineering (Full Publication)

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