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I am an AI Infrastructure Engineer with over 15 years of experience specializing in scalable AI infrastructures and distributed systems. I design robust, production-grade AI platforms and optimize large-scale models and LLM workflows across AWS and GCP. My work emphasizes reliability, observability, and secure deployment of AI solutions at scale. My career spans Mayo Clinic, Nike, and Capital One, where I led cross-functional teams to build cutting-edge AI systems while ensuring privacy, compliance, and real-world performance. I enjoy solving complex AI system challenges and delivering high-demand, real-world capabilities that excel under pressure.…I am an AI Infrastructure Engineer with over 15 years of experience specializing in scalable AI infrastructures and distributed systems. I design robust, production-grade AI platforms and optimize large-scale models and LLM workflows across AWS and GCP. My work emphasizes reliability, observability, and secure deployment of AI solutions at scale. My career spans Mayo Clinic, Nike, and Capital One, where I led cross-functional teams to build cutting-edge AI systems while ensuring privacy, compliance, and real-world performance. I enjoy solving complex AI system challenges and delivering high-demand, real-world capabilities that excel under pressure.

Austin Martinez

AI Engineer, Data Scientist, Full Stack Developer, +4

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I am an AI Infrastructure Engineer with over 15 years of experience specializing in scalable AI infrastructures and distributed systems. I design robust, production-grade AI platforms and optimize large-scale models and LLM workflows across AWS and GCP. My work emphasizes reliability, observability, and secure deployment of AI solutions at scale. My career spans Mayo Clinic, Nike, and Capital One, where I led cross-functional teams to build cutting-edge AI systems while ensuring privacy, compliance, and real-world performance. I enjoy solving complex AI system challenges and delivering high-demand, real-world capabilities that excel under pressure.…I am an AI Infrastructure Engineer with over 15 years of experience specializing in scalable AI infrastructures and distributed systems. I design robust, production-grade AI platforms and optimize large-scale models and LLM workflows across AWS and GCP. My work emphasizes reliability, observability, and secure deployment of AI solutions at scale. My career spans Mayo Clinic, Nike, and Capital One, where I led cross-functional teams to build cutting-edge AI systems while ensuring privacy, compliance, and real-world performance. I enjoy solving complex AI system challenges and delivering high-demand, real-world capabilities that excel under pressure.

Available to hire

I am an AI Infrastructure Engineer with over 15 years of experience specializing in scalable AI infrastructures and distributed systems. I design robust, production-grade AI platforms and optimize large-scale models and LLM workflows across AWS and GCP. My work emphasizes reliability, observability, and secure deployment of AI solutions at scale.

My career spans Mayo Clinic, Nike, and Capital One, where I led cross-functional teams to build cutting-edge AI systems while ensuring privacy, compliance, and real-world performance. I enjoy solving complex AI system challenges and delivering high-demand, real-world capabilities that excel under pressure.

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Language

English

Fluent

Work Experience

Senior Computer Vision & Deep Learning Engineer at Mayo Clinic

January 1, 2020 - Present

Designed and deployed AI-powered medical image analysis pipelines for radiology and pathology, reducing clinician review time by ~35%. Built HIPAA-compliant microservices on Azure AKS and AWS EKS to enable real-time image exchange between EHRs, research platforms, and clinical teams. Developed vision-based identity and document verification models for patient intake and claims validation. Fine-tuned Vision Transformers and CNNs using PyTorch and TensorFlow; deployed low-latency GPU inference with TensorRT and ONNX. Integrated multimodal AI pipelines with structured EHR data and clinical notes; established governance with explainability for regulatory audits. Tracked experiments with MLflow and DVC; served models via FastAPI on Kubernetes for high availability in clinical environments.

Machine Learning Engineer at Nike

July 1, 2014 - December 31, 2018

Built real-time image and document analysis systems for user-generated content and retail submissions with sub-50 ms inference latency. Automated cloud AI infrastructure provisioning using Terraform and CloudFormation. Applied OCR and NLP fusion to extract structured data from product labels and manufacturing documents, improving data accuracy by 60%. Increased test coverage from 50% to 95% and reduced production defects by 70% through automated testing in API-driven ML services. Developed personalized recommendation engines and visual fraud/misuse detection models; created lightweight client-side AI SDKs with WebAssembly; designed explainable AI dashboards for product analytics, compliance, and risk teams.

Full Stack AI/ML Engineer at Capital One

January 1, 2010 - June 30, 2014

Developed real-time video and image analytics tools to support digital onboarding. Built NLP-based text and image classifiers to automate content review and anomaly detection, reducing manual review time. Implemented computer vision models to assess document image quality and detect counterfeit or duplicates, cutting review time. Designed high-throughput APIs for image and document processing with sub-second latency at scale. Led AI platform roadmaps for fraud detection, risk scoring, and digital identity verification. Enabled active-learning feedback loops for analysts to correct outputs in production, improving retraining cycles. Modernized legacy applications and built secure microservices processing large transaction volumes; integrated with banking and underwriting systems; ensured PCI DSS, SOX, and internal governance compliance; established tokenized audit trails for traceability.

Senior AI Infrastructure Engineer at Mayo Clinic

January 1, 2020 - Present

Designed and implemented scalable AI systems powering patient engagement, provider workflows, and clinical operations at scale, ensuring production-grade reliability and performance. Led the development of AI foundations that support LLM integration and large-scale AI workflows in regulated healthcare environments, incorporating OpenAI and foundational models. Engineered distributed systems on AWS and GCP for deploying and scaling AI-powered solutions, including multi-node distributed training and GPU optimization for real-time patient data analysis. Integrated AI orchestration frameworks (LangChain, Haystack) to enable retrieval-augmented generation, function calling, and memory routing for clinical decision support. Architected end-to-end MLOps and observability pipelines, automating model evaluation, retraining, and deployment. Designed secure, HIPAA/GDPR-compliant AI solutions and implemented AI-enabled human-in-the-loop processes. Developed APIs and microservices for low-latency A