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Manuel Precious

@manuelprecious

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Research Scientist | Machine Learning Engineer | ML Infrastructure

United States

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What I'm looking for

I want to build reliable, production-grade ML systems—reproducing and evaluating research, optimizing training/inference (GPU + pipelines), and deploying scalable services that keep working after deployment.

Artificial Intelligence has reached a point where building a model is only part of the problem. Understanding why it works, where it fails, and how to make it reliable outside a notebook is where the real engineering begins.

Reading research papers, reproducing architectures, building experiments, and turning those ideas into working applications is the direction I've committed to. Reinforcement learning, computer vision, natural language processing, representation learning, retrieval, and production machine learning are the areas I keep coming back to.

Most projects start with a paper, an engineering problem, or a question that needs an answer. The process usually ends with an implementation, evaluation, benchmarking, and a deployable application.

The engineering around machine learning matters just as much as the model itself. Training pipelines, model evaluation, inference optimization, GPU computing, deployment, and production infrastructure are all part of building AI that continues to work after deployment.

Everything I build, experiment with, or learn eventually finds its way into Mac Sterlin, where I document research, implementations, production projects, and technical writing as they evolve.

Areas of Interest

Machine Learning

Deep Learning

Reinforcement Learning

Computer Vision

Natural Language Processing

Representation Learning

Transformer Architectures

Retrieval Systems

Production Machine Learning

MLOps

Technologies

Python • PyTorch • TensorFlow • CUDA • Scikit-Learn • NumPy • Pandas • Docker • Kubernetes • FastAPI • MLflow • Linux

Experience

Work history, roles, and key accomplishments

Current

Machine Learning Engineer & MLOPS

Current

Mac Sterlin

Mar 2026 - Present (5 months)

Skills used: Python, Docker, Kubernetes, MLflow, FastAPI, PyTorch, TensorFlow, CUDA, Linux, Scikit-learn, NumPy, Pandas

Description

Building and operationalizing machine learning systems for representation learning, ranking, multimodal understanding, reinforcement learning, and production inference. Designing reproducible training pipelines, optimizing GPU workloads, deploying scalable inference

MLOPS & BACKEND ENGINEER

Cognizant

Mar 2024 - May 2026 (2 years 2 months)

Skills used: Python, CatBoost, Scikit-learn, MLflow, Prefect, Evidently AI, GitHub Actions, PostgreSQL, Docker, HAProxy

Architected a high-performance behavioral intelligence pipeline designed to process and ingest 85,000+ streaming telemetry payloads per minute for real-time inference and pattern analysis.

Engineered automated feature engineering workflows and time-series aggregations within a

Python CatBoost scikit learn MLFlow Prefect Dataflow Automation Evident PostgreSQL Docker Prefect Evidently AI GitHub Actions HAProxy Prometheus CI CD Feature Engineering Monitoring Monitoring Drift

MACHINE LEARNING & BACKEND ENGINEER

Citrix

Oct 2023 - Aug 2024 (10 months)

Skills used: Python, Scikit-learn, Decision Trees, Flask, PostgreSQL, Express, JavaScript, Log
Parsing

● Developed an intelligent log analysis and anomaly detection parsing tool in Python,
using Scikit-learn to clean raw telemetry datasets and isolate system failure patterns.

● Programmed automated data validation scripts and Exploratory Data Analysis (EDA)
pipelines to clean unstructured log fi

Python PostgreSQL MLOps scikit learn Decision Trees Flask JavaScript EDA REST APIs CI CD Data Validation Feature Engineering

Machine Learning Engineer

Citrix

Aug 2022 - Oct 2023 (1 year 2 months)

Skills used: Python, C++, Artificial Neural Networks, Docker, MySQL, Node.js, TypeScript

● Developed Intelligent Routing Frameworks: Implemented mathematical load-aware
traffic routing algorithms and predictive scheduling configurations in Python and
Node.js for optimal data packet distribution.

● Built data preprocessing and feature extraction modules to feed historical traffic logs
into Neural

Python Neural Network Architecture Design Docker TensorFlow C++MySQL Neural Networks Node.Js Development TypeScript Load Balancing CI CD DDoS Pattern Detection

Education

Degrees, certifications, and relevant coursework

University of Waterloo

Master of Mathematics, Computer Science (Artificial Intelligence/Machine Learning)

2023 - 2025

Completed an MMath in Computer Science with a specialization in Artificial Intelligence and Machine Learning.

ISBAT University

Bachelor of Science, Artificial Intelligence and Machine Learning

2019 - 2022

Grade: 4.97 CGPA

Specialized in advanced neural network architectures and computer vision, developing and optimizing Spatial Domain Transformation models for real-time image processing and environmental data extraction. Conducted research on efficient sorting/search algorithms in large-scale distributed databases and focused on high-performance computing (HPC) for AI, maintaining a 4.97 CGPA.