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Ch Omar

@chomar

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Lead Machine Learning Engineer specializing in end-to-end ML, LLM solutions, and scalable MLOps automation.

United States

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What I'm looking for

I’m looking for a team where I can own end-to-end ML/LLM delivery—model architecture, production serving, and MLOps automation—with strong governance, monitoring, and room to scale reliable intelligence systems.

I’m a Senior/Lead Machine Learning Engineer with 9+ years of experience designing, training, and deploying production-scale ML systems across automated platforms, financial modeling, and enterprise analytics. I focus on end-to-end ML workflows—from model architecture and feature engineering through evaluation, deployment, and continuous operations.

I build high-performance models and scalable ML services using modern frameworks such as PyTorch, TensorFlow, and XGBoost, including transformer-based pipelines. I’m especially strong in creating robust, reliable model behavior using gradient boosting, decision trees, neural networks, and deep learning approaches.

On the engineering side, I deliver cloud-native deployments and MLOps automation with CI/CD pipelines and governance for production reliability. I’ve implemented model serving with REST APIs, microservices, distributed model serving, and serving frameworks such as TensorFlow Serving and TorchServe, while adding monitoring, drift detection, and lineage tracking to keep systems dependable at scale.

I also lead evaluation of emerging ML algorithms and LLM techniques to strengthen platform capabilities. My projects include an ASR fine-tuning pipeline using HuggingFace and Wav2Vec2 (sub 0.2 WER) and an LLM Document QA system with RAG (LangChain, ChromaDB) deployed as a REST API with under 2 second response time.

Experience

Work history, roles, and key accomplishments

Current

Lead Machine Learning Engineer

Current

Eco-Green Developers

Jan 2024 - Present (2 years 6 months)

Led design and deployment of end-to-end ML systems for automated inference, optimization, and enterprise intelligence use cases. Developed ML and LLM-driven models and built production MLOps pipelines with monitoring and governance controls.

Machine Learning LLM PyTorch TensorFlow XGBoost MLOps MLFlow Docker Kubernetes CI CD Monitoring Lineage Tracking Governance Inference Optimization

Senior Machine Learning Engineer

AlignerBase

Nov 2020 - Dec 2023 (3 years 1 month)

Engineered ML and DL models for automated classification, anomaly detection, and enterprise analytics workflows. Built scalable preprocessing and feature pipelines and implemented distributed training and cloud-native model deployment/serving.

Machine Learning Deep Learning Classification Anomaly Detection Feature Engineering Distributed Training GPU Clusters Explainable AI Regulatory Compliance Cloud Native Model Serving Pipelines AutoML Model Evaluation

Machine Learning Engineer II

Wings Intranet

Sep 2018 - Oct 2020 (2 years 1 month)

Built credit risk, classification, and document intelligence models using traditional ML and neural architectures. Deployed models into microservice-based scoring systems and implemented monitoring, drift detection, and model validation via experiments.

Monitoring Drift Machine Learning Neural Networks Credit Risk Modeling Classification Document Intelligence Feature Engineering Scalable Preprocessing Pipelines Regulatory Grade Data Microservices Monitoring Data Ingestion data transformation A B Testing Model Comparison

Machine Learning Engineer I

Ayasdi

Jul 2016 - Aug 2018 (2 years 1 month)

Developed clustering, anomaly detection, and pattern-mining models for enterprise datasets. Built modular data ingestion and evaluation pipelines and supported production deployments by optimizing inference and improving ML tooling and reproducibility.

Machine Learning Clustering Anomaly Detection Statistical Methods Data Pipelines Feature Preparation Inference Optimization Technical Documentation