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I'm Max Norris, a Senior AI/ML Engineer with 8+ years of experience in AI, NLP, computer vision, and MLOps. I design, train, fine-tune, and deploy scalable ML and deep learning systems using PyTorch, TensorFlow, and AWS SageMaker. I love turning hard problems into reliable, production-ready solutions that deliver real value. I specialize in LLM optimization, RAG architectures, vector databases, feature engineering, and cloud-native deployment. I bring a strong foundation in data science, statistical modeling, and building production-grade AI systems across multiple domains.…I'm Max Norris, a Senior AI/ML Engineer with 8+ years of experience in AI, NLP, computer vision, and MLOps. I design, train, fine-tune, and deploy scalable ML and deep learning systems using PyTorch, TensorFlow, and AWS SageMaker. I love turning hard problems into reliable, production-ready solutions that deliver real value. I specialize in LLM optimization, RAG architectures, vector databases, feature engineering, and cloud-native deployment. I bring a strong foundation in data science, statistical modeling, and building production-grade AI systems across multiple domains.

Max Norris

AI Engineer, Data Scientist, Full Stack Developer, +3

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I'm Max Norris, a Senior AI/ML Engineer with 8+ years of experience in AI, NLP, computer vision, and MLOps. I design, train, fine-tune, and deploy scalable ML and deep learning systems using PyTorch, TensorFlow, and AWS SageMaker. I love turning hard problems into reliable, production-ready solutions that deliver real value. I specialize in LLM optimization, RAG architectures, vector databases, feature engineering, and cloud-native deployment. I bring a strong foundation in data science, statistical modeling, and building production-grade AI systems across multiple domains.…I'm Max Norris, a Senior AI/ML Engineer with 8+ years of experience in AI, NLP, computer vision, and MLOps. I design, train, fine-tune, and deploy scalable ML and deep learning systems using PyTorch, TensorFlow, and AWS SageMaker. I love turning hard problems into reliable, production-ready solutions that deliver real value. I specialize in LLM optimization, RAG architectures, vector databases, feature engineering, and cloud-native deployment. I bring a strong foundation in data science, statistical modeling, and building production-grade AI systems across multiple domains.

Available to hire

I’m Max Norris, a Senior AI/ML Engineer with 8+ years of experience in AI, NLP, computer vision, and MLOps. I design, train, fine-tune, and deploy scalable ML and deep learning systems using PyTorch, TensorFlow, and AWS SageMaker. I love turning hard problems into reliable, production-ready solutions that deliver real value.

I specialize in LLM optimization, RAG architectures, vector databases, feature engineering, and cloud-native deployment. I bring a strong foundation in data science, statistical modeling, and building production-grade AI systems across multiple domains.

Skills

Experience Level

Expert

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Intermediate

Language

English

Fluent

Work Experience

Senior AI/ML Engineer at Pixolat LLC

January 1, 2023 - Present

Led multilingual NLP pipeline development and MLOps automation; architected retrieval-augmented generation (RAG) systems with LangChain and vector databases; designed fast, scalable LLM inference microservices on AWS SageMaker handling 1M+ monthly requests with 99.9% uptime. Implemented real-time facial recognition and 3D CNN-based scene reconstruction; optimized cloud infrastructure and container orchestration to reduce operational costs by ~30%.

Senior AI/ML Engineer at Google

January 1, 2019 - December 1, 2022

Enhanced documentation classification F1 score by 35% using transformer-based NLP models and embedding optimization; accelerated semantic search performance by 40% via scalable vector indexing pipelines; built LS TM-based anomaly detection engines; established MLOps workflows for retraining, validation, and monitoring, reducing system downtime by 30%; deployed BER T-based NER models improving entity extraction; implemented real-time incident detection with YOLO/OpenCV, cutting emergency response time by 35%.

Python Developer at PaklogicS

January 1, 2016 - December 1, 2018

Delivered 5+ scalable Flask and Django apps for healthcare and logistics platforms supporting 10,000+ monthly active users. Built 3 ETL pipelines with Pandas and PySpark processing 500K+ records daily across 8+ data sources, reducing ingestion time by 40%. Optimized large-scale web data extraction with Selenium, BeautifulSoup, and Scrapy, increasing throughput by 25%.

Senior AI/ML Engineer at Google

January 1, 2023 - Present

Currently engineering multilingual NLP pipelines using BERT and GPT architectures, increasing classification accuracy by 30%. Architected Retrieval-Augmented Generation (RAG) systems with LangChain and vector databases, boosting semantic search relevance by 35%. Designed FastAPI-based microservices for LLM inference, handling 1M+ monthly requests with 99.9% uptime. Implemented automated ML pipelines on AWS SageMaker, reducing model training cycle time by 60%. Deployed facial recognition and scene reconstruction solutions using YOLOv3 and Twilio APIs, raising detection precision by 20%. Optimized cloud infrastructure and container orchestration, lowering AWS costs by 30%.

AI/ML Engineer at Google

January 1, 2019 - December 1, 2022

Enhanced documentation classification F1 score by 35% through Transformer-based NLP models and embedding optimization. Accelerated semantic search performance by 40% via scalable vector indexing pipelines. Constructed LSTM-based anomaly detection engines, improving risk prediction accuracy by 20%. Established MLOps workflows for retraining, validation, and monitoring, decreasing system downtime by 30%. Delivered BERT-based NER models, improving entity extraction precision by 20%. Implemented real-time incident detection systems using YOLOv3 and Twilio APIs, reducing emergency response time by 35%.

Python Developer at Paklogic

January 1, 2016 - December 1, 2018

Delivered 5+ scalable Flask and Django apps for health care and logistics platforms, supporting 10,000+ monthly active users. Constructed 3 ETL pipelines with Pandas and PySpark, processing 500K+ records daily across 8+ data sources, reducing ingestion time by 40%. Optimized large-scale web data extraction using Selenium, BeautifulSoup, and Scrapy, increasing throughput by 25%.