Senior AI Engineer

Christine Straub

Applied AI Engineer — AI Systems Architecture — MLOps — LLMOps

949-***-**** # ****************@*****.*** ï straubchristine § christinestraub christinemstraub San Clemente, CA

Summary

Applied AI Engineer with 8+ years building production AI systems across defense, healthcare, finance, and other enterprise domains, spanning from classified DoD intelligence systems, HIPAA-compliant healthcare applications, and enterprise-scale document processing. Specializes in applied agentic workflows, LLM/VLM productization, and MLOps/LLMOps infrastructure, with deep expertise designing advanced RAG pipelines, distributed edge AI architectures, and multi-agent orchestration systems. Education

University of California, Berkeley Berkeley, CA

Bachelor of Arts in Computer Science December 2017 University of California, Berkeley Berkeley, CA

Bachelor of Arts in Cognitive Science December 2017 Technical Skills

Agentic AI & GenAI Expertise: Multi-Agent Systems (MAS), Agentic Workflows, RAG (GraphRAG/HyDE), Vision-Language Models (VLMs), ReAct, Chain-of-Thought (CoT), Prompt Optimization (DSPy), Model Context Protocol

(MCP), Autonomous Agents

ML/AI/NLP Expertise: Computer Vision (Object Detection/Segmentation), Natural Language Processing (NLP), Transformer Architectures (BERT/ViT), Reinforcement Learning (RL), Model Fine-tuning (LoRA/QLoRA), Quantization

(GGUF/AWQ), OCR, Edge AI, Distributed Training

Agentic Frameworks & LLMs: LangGraph, CrewAI, Microsoft AutoGen, Pydantic AI, LangChain, LlamaIndex, OpenAI Swarm, Claude 4 (Sonnet/Opus), GPT-4o/o1/o3, Gemini 2.0 (Flash/Pro), Llama 3.x, Ollama, vLLM LLM Evaluation & Red-Teaming: Ragas (RAG Assessment), LangSmith, Arize Phoenix, DeepEval, Human-in-the-loop

(HITL) Evaluation, Hallucination Detection, Bias & Safety Testing Deep Learning & MLOps: PyTorch, TensorFlow, Keras, Hugging Face Transformers, ONNX, TensorRT, Metaflow, Weights & Biases, MLflow, Ray Serve

Computer Vision: YOLO (v8/v9/NAS), Detectron2, Tesseract, PaddleOCR, OpenCV, FiftyOne, Encord, Google ML Kit, SAM (Segment Anything Model)

Cloud & Infrastructure: AWS (Lambda, SageMaker, Bedrock, ECS, EventBridge), GCP (Vertex AI, Cloud Run, BigQuery, Document AI), Kubernetes (K8s), Docker, Terraform Fullstack & Databases: Python, Node.js, TypeScript, Rust, PostgreSQL, MongoDB, Pinecone/Weaviate/ChromaDB

(Vector Search), Elasticsearch, Kafka

Work Experience

Senior Applied AI Engineer July 2025 – Present

Medici Land Governance Washington, DC

Develops blockchain-based land administration and titling systems to modernize property ownership records.

• Cost-Optimized VLM Routing System: Designed dynamic routing layer leveraging Claude Sonnet 4, GPT-4o, and Gemini 2.0, reducing inference costs by 70% while maintaining 95%+ accuracy on complex handwritten legal documents

(deeds, liens, court records).

• Historical Document Processing: Developed custom deep learning pipeline for 300,000+ historical court dockets

(1850s-era) with specialized column detection, cutting manual review requirements by 40%.

• Hybrid OCR Pipeline: Engineered production-scale fallback system combining PaddleOCR with Claude Vision, achieving 97% accuracy via automated quality assessment and intelligent model selection for property record extraction.

• Core Technologies: Claude Sonnet 4, GPT-4o, Gemini 2.0, GCP (Cloud Run, BigQuery, Vertex AI Studio, Document AI, GKE), PaddleOCR, Kafka, Metaflow, Elasticsearch. Senior Machine Learning Engineer (CV/Edge AI) November 2024 – July 2025 RIOS Intelligent Machines Palo Alto, CA

Builds computer vision and ML infrastructure for robotic process automation in manufacturing and industrial environments.

• End-to-End MLOps Pipeline: Architected Metaflow workflow reducing deployment time by 70%, enabling 60 FPS real-time inference via Kubernetes auto-scaling for industrial video anomaly detection processing 10M+ daily images.

• Active Learning Loop: Integrated FiftyOne and Encord to automate data selection, reducing manual annotation time by 60% via uncertainty sampling and bidirectional annotation-dataset pipeline.

• Edge AI Optimization: Designed custom quantized computer vision operators (YOLO v8/v9), achieving 5x inference speedup on edge devices (Jetson/Orin) through ONNX/TensorRT optimization.

• GPU Training Infrastructure: Implemented GPU-optimized mixed-precision training (FP16/BF16) in PyTorch, increasing throughput by 40%.

• Core Technologies: YOLO v8/v9, Metaflow, FiftyOne, Encord, PyTorch, Kubernetes, Docker, Weights & Biases, ONNX, TensorRT.

Senior Applied AI Engineer May 2023 – April 2025

Unstructured IO San Francisco, CA

Develops open-source and API-based tools that transform unstructured documents into structured data.

• Multi-Agent AI System: Architected production Pydantic AI + MCP (Model Context Protocol) orchestration system, creating custom MCP servers interfacing with unstructured APIs for intelligent document processing workflows.

• VLM Benchmarking & Integration: Evaluated 10+ VLM providers (Claude 3.5/3.7, GPT-4o, Gemini 1.5/2.0), engineered prompt optimization techniques dramatically improving table structure recognition and image text extraction accuracy.

• Layout Detection Fine-Tuning: Fine-tuned layout models (YOLOX, YOLO-NAS, Detectron2) on 11,000+ technical PDFs, achieving 10% accuracy increase and 13% reduction in missing text detection.

• OCR Pipeline Optimization: Led OCR enhancement across Tesseract/PaddleOCR, implementing preprocessing

(scaling, contrast, PSM optimization) improving accuracy by 15% and reducing missing text by 10%.

• Document Layout Analysis: Engineered sophisticated element extraction pipeline separating images, figures, and tables as standalone base64-encoded blocks, implementing xy-cut bounding-box sorting algorithm for improved element ordering and document reconstruction.

• Production Impact: Resolved 300+ bugs, reviewed 500+ PRs, implemented memory optimizations (chunked PDF processing), intelligent encoding detection for enterprise-scale processing.

• Core Technologies: YOLOX/YOLO-NAS/Detectron2, Tesseract/PaddleOCR, OpenCV, LangChain, LlamaIndex, Claude/GPT/Gemini APIs, Pydantic AI.

Lead Software Engineer – AI/ML (DoD) Jan 2021 – Feb 2024 Sapient Logic San Diego, CA

AI-enabled intelligence systems for the Department of Defense across multiple classified programs.

• Mobile Intelligence Translation App: Architected mobile OCR platform enabling real-time document processing in air-gapped field environments using Google ML Kit + Tesseract4Android for French/Arabic/Chinese materials during sensitive site exploitation operations.

• AI-Powered Intelligence Management Platform: Led development of microservices-based intelligence requirements system, engineering semi-automated validation workflows and ML-powered classification that streamlined intelligence processing across security classifications, integrating with GCGS-J, ICSF, and Tactical Awareness Kit.

• Cybersecurity Threat Intelligence App: Developed BERT/SBERT-based semantic similarity engine automating MITRE ATT&CK framework mapping to defense mechanisms, reducing manual security gap analysis time by 94% through multi-vector NLP pipeline (Word2Vec, GloVe, Transformers).

• Secure Cross-Domain Architecture: Designed one-way secure transfer mechanisms for classified environments, implemented military-grade encryption protocols, and established DevSecOps CI/CD pipelines with security documentation for ATO compliance.

• Technical Leadership: Directed cross-functional teams through full SDLC for classified systems, created architecture documentation for defense applications in network-denied tactical environments, and established test management frameworks for multiple DoD programs.

• Core Technologies: Google ML Kit, Tesseract OCR, BERT/SBERT, PyTorch, Word2Vec/GloVe, Microservices, Docker, React, MITRE ATT&CK, Air-gapped systems, Cross-domain solutions. Lead Software Engineer – AI/ML Infrastructure April 2021 – Feb 2023 Sapient Logic San Diego, CA

HIPAA-compliant Electronic Health Record system for hospital patient management.

• HIPAA-Compliant Cloud Architecture: Architected complete EHR solution on AWS (EC2, RDS, VPC, ECS, ECR) with comprehensive security controls including KMS encryption, MFA (Vonage/SendGrid), role-based access, audit logging via CloudWatch.

• Network Security & Infrastructure: Deployed Sonicwall Firewall with secure VPC architecture, SSL certificates for encrypted transfers, automated alarm systems for RDS/EC2 monitoring across Nginx/Django stack.

• Patient Data Management: Engineered comprehensive platform tracking medical histories, demographics, documents from registration through discharge, with real-time ER analytics dashboards for patient flow optimization.

• Healthcare Interoperability: Integrated CollaborateMD, eClaimStatus, epowerdoc using HL7/FHIR protocols for seamless patient management and claims processing.

• Technical Leadership & Team Management: Led engineering team implementing physician-requested improvements, conducted technical interviews and hired full-stack, DevOps, and QA engineers, created comprehensive product requirement documentation, and established GitHub Actions CI/CD pipeline with DevOps collaboration.

• Core Technologies: AWS (EC2, RDS, VPC, ECS, ECR, CloudWatch), Django, Vue.js, React, PostgreSQL, HL7/FHIR, Docker, GitHub Actions.

AI Software Architect June 2022 – June 2023

Speechlab AI San Francisco, CA

AI-powered audio/video localization platform enabling speech-to-speech translation and synthetic dubbing.

• Cloud-Native Localization Architecture: Designed end-to-end AWS serverless infrastructure (Lambda, EventBridge, ECS, App Runner, S3) for event-driven media processing, reducing dubbing costs by 80% and accelerating timelines from weeks to hours.

• ML API Gateway: Built scalable API layer integrating speech recognition, machine translation, and text-to-speech ML models with performance optimization and model monitoring for reliable multilingual workflows.

• Secure Media Pipeline: Engineered multipart upload system for large files, automated subtitle generation via Lambda, audio-text synchronization, direct-from-S3 secure content delivery with granular access controls.

• Full-Stack Platform: Architected Node.js/Express.js backend with React/Next.js/TypeScript frontend, implementing AWS Cognito authentication, Paddle monetization, permission-based sharing via SES.

• Core Technologies: AWS (Lambda, EventBridge, S3, ECS, App Runner, Cognito, CloudWatch, SES), Node.js, Express.js, React, Next.js, TypeScript, MongoDB.

Machine Learning Engineer May 2021 – May 2022

Collegis Education Chicago, IL

Data analytics solutions optimizing enrollment growth and student success for higher education institutions.

• Event-Driven ETL Pipeline: Architected near-real-time pipelines using Google Cloud Functions/Cloud Run ingesting student interaction data from Phoneburner, Five9, LMS Canvas, reducing data latency by 85%.

• Modern Data Stack: Orchestrated multi-source integration via Fivetran and DBT for BigQuery modeling, implementing automated schema evolution and 100% data lineage for governance.

• Speech Analytics: Engineered Call Center Intelligence System using Cloud Speech-to-Text and Natural Language APIs for automated transcription and sentiment analysis.

• Core Technologies: GCP (Cloud Functions, Run, BigQuery, Speech-to-Text, Natural Language API), DBT, Fivetran, Python, ThoughtSpot.

Software Engineer September 2017 – April 2021

Moody's Analytics Silicon Valley, CA

Catastrophe risk modeling and location intelligence platforms for insurance and financial services clients.

• Multi-Product Engineering: Contributed to RMS(one), RiskLink, and RiskBrowser platforms, building client-facing features for catastrophe risk assessment and portfolio analysis enabling insurers to evaluate multi-billion dollar portfolios.

• Geospatial Data Pipelines: Engineered data processing pipelines for location intelligence products, enabling portfolio managers to visualize asset exposure against natural disaster probability zones (hurricanes, earthquakes, floods) and hazard models.

• Core Technologies: Java, TypeScript, Python, Geospatial visualization, Data pipelines, Risk modeling platforms.

Certifications

Deep Learning Specialization (DeepLearning.AI, Stanford) Machine Learning Specialization (DeepLearning.AI, Stanford) MLOps Specialization (DeepLearning.AI) AWS Cloud Practitioner (AWS) IBM Data Science (IBM) Google Data Analytics (Google) Business Intelligence (Google)

Contact this candidate