Mark

Mark led the design of scalable MLOps pipelines using MLflow and Kubeflow on AWS EKS, and built a RAG pipeline for a crypto trading AI agent. He created a Model Context Protocol infrastructure to enable real-time model coordination, implemented trading agents in OpenAI Gym with DPO, and fine-tuned LLMs using AWS SageMaker.
MLflow, Kubeflow, FastAPI, Kafka, AWS SageMaker, RAG, LSTM, Prophet, ARIMA, ETL, Airflow, Spark

Mark improved recommendation systems using vector search and Elasticsearch, increasing engagement by 15%. He fine-tuned CLIP for product classification, implemented SageMaker pipelines for fraud detection using NLP on customer chats, and deployed client-side ML with TensorRT and Firebase.
Elasticsearch, CLIP, SageMaker, TensorRT, Tableau, SQL, A/B Testing

Mark launched an AWS-hosted e-commerce platform with multiple API integrations. He deployed TensorFlow Lite models, streamlined data pipelines, and reduced model deployment time by 30% with MLflow integration.
AWS, MLflow, TensorFlow Lite, Django, React, API Integration

Mark built SaaS platforms and payment systems using NestJS and GraphQL, deploying them to GKE with GitLab CI/CD. He also developed component libraries, maintained full-stack codebases, and enforced testing with Jest, Mocha, and Cypress.
NestJS, GraphQL, React, Node.js, GitLab CI/CD, Docker, AWS Lambda, ECS, ECR