I design and deliver machine learning and MLOps systems — from distributed data pipelines to production-grade models that scale. This is a showcase of my work at the intersection of research and engineering.
I'm an AI engineer focused on building data-driven, distributed systems — designing robust architectures, optimizing ML workflows, and deploying intelligent solutions that perform reliably at scale.
My background spans distributed computing, streaming algorithms, and large-scale data processing, with deep hands-on experience across the full ML lifecycle: from feature engineering and model training to experiment tracking, containerization, and CI/CD.
Deep learning, NLP, model training & evaluation
MLflow, Docker, CI/CD, reproducible training
Spark, MapReduce, streaming, large-scale data
The tools and frameworks I use to build, train, and ship intelligent systems.
A selection of AI and systems projects — from distributed ML pipelines to applied research.
End-to-end sentiment analysis pipeline pairing Spark MapReduce TF-IDF feature engineering with PyTorch deep models (LSTM / Transformer / BERT), tracked via MLflow and containerized with Docker.
View case studyAI portfolio optimization built on PySpark + PyTorch — Modern Portfolio Theory optimizers, Deep RL (DDPG/PPO) rebalancing, and Monte Carlo risk simulations with realistic backtesting.
View case studyFull-stack job platform with an LLM-powered A/B testing framework comparing original vs. AI-enhanced job descriptions, with conversion analytics, dual auth, and a Dockerized CI/CD deploy.
View case studyAI visual assistant for visually impaired users — real-time object detection and navigation guidance powered by OpenCV and a Python inference backend.
View case studyHands-on engineering across QA, DevOps, and ML — backed by rigorous CS foundations.
Across 5+ internships, built and shipped production-ready systems — automated testing infrastructure, CI/CD pipelines, and ML-driven features from concept to deployment.
Advanced study in distributed computing, streaming algorithms, and large-scale data processing — alongside core coursework in algorithms, databases, OS, and networking.
Always happy to trade notes on AI, ML, and distributed systems, or to talk through any of the work shown here. Reach out anytime.
enockmecheo@nyu.edu