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Hi, I’m Michael Murphy, a Senior AI Engineer with over a decade of experience building, deploying, and optimizing scalable AI/ML models, solutions, and infrastructure. I specialize in deep learning, NLP, and computer vision, and I enjoy designing end-to-end data pipelines and production-grade ML systems on AWS, GCP, and Kubernetes. I’m passionate about turning data into actionable insights and delivering reliable AI services that enhance user experiences. In my roles at Stripe, Snap, and Amazon, I’ve led cross-functional teams, built scalable MLOps pipelines, and delivered measurable impact across real-time analytics, personalization, and automation.…Hi, I’m Michael Murphy, a Senior AI Engineer with over a decade of experience building, deploying, and optimizing scalable AI/ML models, solutions, and infrastructure. I specialize in deep learning, NLP, and computer vision, and I enjoy designing end-to-end data pipelines and production-grade ML systems on AWS, GCP, and Kubernetes. I’m passionate about turning data into actionable insights and delivering reliable AI services that enhance user experiences. In my roles at Stripe, Snap, and Amazon, I’ve led cross-functional teams, built scalable MLOps pipelines, and delivered measurable impact across real-time analytics, personalization, and automation.

Michael Murphy

Full Stack Developer, Web Developer, Programmer, +4

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Hi, I’m Michael Murphy, a Senior AI Engineer with over a decade of experience building, deploying, and optimizing scalable AI/ML models, solutions, and infrastructure. I specialize in deep learning, NLP, and computer vision, and I enjoy designing end-to-end data pipelines and production-grade ML systems on AWS, GCP, and Kubernetes. I’m passionate about turning data into actionable insights and delivering reliable AI services that enhance user experiences. In my roles at Stripe, Snap, and Amazon, I’ve led cross-functional teams, built scalable MLOps pipelines, and delivered measurable impact across real-time analytics, personalization, and automation.…Hi, I’m Michael Murphy, a Senior AI Engineer with over a decade of experience building, deploying, and optimizing scalable AI/ML models, solutions, and infrastructure. I specialize in deep learning, NLP, and computer vision, and I enjoy designing end-to-end data pipelines and production-grade ML systems on AWS, GCP, and Kubernetes. I’m passionate about turning data into actionable insights and delivering reliable AI services that enhance user experiences. In my roles at Stripe, Snap, and Amazon, I’ve led cross-functional teams, built scalable MLOps pipelines, and delivered measurable impact across real-time analytics, personalization, and automation.

Available to hire

Hi, I’m Michael Murphy, a Senior AI Engineer with over a decade of experience building, deploying, and optimizing scalable AI/ML models, solutions, and infrastructure. I specialize in deep learning, NLP, and computer vision, and I enjoy designing end-to-end data pipelines and production-grade ML systems on AWS, GCP, and Kubernetes. I’m passionate about turning data into actionable insights and delivering reliable AI services that enhance user experiences.

In my roles at Stripe, Snap, and Amazon, I’ve led cross-functional teams, built scalable MLOps pipelines, and delivered measurable impact across real-time analytics, personalization, and automation.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

English

Fluent

Work Experience

Senior Software Engineer at Stripe

May 1, 2023 - December 1, 2025

Led design and deployment of real-time predictive analytics models using TensorFlow and PyTorch to forecast user behavior, enabling more personalized recommendations. Built NLP capabilities with Hugging Face Transformers (BERT, GPT) for improved search and recommendations. Implemented scalable ETL/ELT pipelines with Airflow, Databricks, Pandas, Kafka, and RabbitMQ, with strict data lineage and reliability. Deployed models via AWS SageMaker, orchestrating services on ECS and S3; automated deployment and retraining using Docker, Kubernetes, and GitHub Actions. Built explainable AI pipelines and integrated computer vision (YOLO) into video metadata tagging. Led MLOps patterns with MLflow, Terraform, and Kubeflow-inspired workflows.

Senior Software Engineer at Snap Inc.

February 1, 2019 - April 1, 2023

Developed customer engagement AI agents using NLP (spaCy, LangChain, LangGraph, Hugging Face) to automate support and reduce response times. Deployed ML models for predicting customer satisfaction from interaction data using Scikit-learn and XGBoost. Built real-time data pipelines with Apache Kafka and Airflow for model training and inference. Implemented AI-powered image and text recognition with OpenCV and TensorFlow; created scalable REST APIs with Django and FastAPI; containerized services with Docker and Kubernetes. Led A/B testing and model evaluation to optimize engagement and platform performance.

Software Development Engineer II at Amazon

January 1, 2014 - December 1, 2018

Designed and deployed end-to-end deep learning and ML pipelines for computer vision, NLP, and time-series tasks. Implemented state-of-the-art architectures (CNNs, RNNs, Transformers) and built scalable data pipelines for medical records with SQL, Pandas, and Dask. Developed production-grade services with PyTorch and TensorFlow, using ONNX/TensorRT for efficient inference; monitored with Prometheus and Grafana.

SDE Intern at Amazon Web Services

June 1, 2013 - December 1, 2013

Contributed to large-scale Python/Node.js applications, modernizing legacy interfaces, and building REST APIs for availability lookup and pricing engines. Implemented monitoring with OpenTelemetry, Prometheus, and Grafana; optimized SQL queries in PostgreSQL/MySQL; collaborated in an 8-person team to deliver scalable, high-performance services.

SDE II at Amazon

January 1, 2014 - December 1, 2018

Designed, developed, and deployed end-to-end deep learning models for computer vision, NLP, and time-series forecasting; implemented architectures including CNNs, RNNs, Transformers, and diffusion models; built scalable data ingestion pipelines for medical records and downstream ML tasks; leveraged PyTorch and TensorFlow for scalable training with hyperparameter tuning and mixed-precision training; developed synthetic data and custom datasets to address data gaps; deployed models into production using ONNX, TensorRT, and RESTful APIs with monitoring via Prometheus and Grafana.