Open to opportunities

Asker Hamza

@askerhamza1

Lead Machine Learning Engineer building scalable LLM/NLP systems and low-latency inference pipelines with strong MLOps expertise.

United States

Message

What I'm looking for

I’m looking for a team where I can lead production-grade LLM/NLP and predictive systems—owning end-to-end MLOps, optimizing distributed training and low-latency inference, and mentoring engineers to ship reliable, enterprise-ready AI.

I’m a Lead Machine Learning Engineer with 10+ years of experience building and scaling production AI systems, specializing in LLMs, NLP, predictive modeling, and low-latency inference. I translate research into enterprise-ready solutions, with deep strength in MLOps, distributed training, and cloud-native architectures across AWS, GCP, and Azure.

Most recently at Togal.AI, I designed transformer-based NLP and recommendation systems for millions of daily interactions, and built real-time inference pipelines with Triton Inference Server to deliver sub-30ms latency. Earlier, I led anomaly detection and zero-shot NLP pipelines at DataRobot, and supported blockchain auditing and asset tokenization workflows at EPICA, while starting with churn and customer lifetime value modeling at Levatas—always focused on reliability, reproducibility, and measurable impact.

Experience

Work history, roles, and key accomplishments

Current

Lead Machine Learning Engineer

Current

Togal.AI

May 2025 - Present (1 year 3 months)

Designed and deployed transformer-based NLP and recommendation systems supporting millions of daily user interactions. Built low-latency real-time inference pipelines and an enterprise MLOps platform, and mentored ML engineers to improve deployment quality and reproducibility.

Recommendation Systems Distributed Training Triton Inference Server MLOps Model Registry Model Monitoring Mentorship

Senior Machine Learning Engineer

DataRobot

Jun 2020 - Apr 2025 (4 years 10 months)

Built anomaly detection and zero-shot NLP classification pipelines, reducing manual labeling needs. Developed real-time inference systems and automated retraining workflows, and benchmarked TorchServe vs ONNX Runtime to improve throughput and reduce resource usage.

Anomaly Detection Zero Shot NLP Classification Kafka fastAPI Redis Airflow TorchServe ONNX Runtime Benchmarking

AI/ML Engineer

EPICA

Jan 2018 - May 2020 (2 years 4 months)

Built ML pipelines for blockchain auditing and asset tokenization, including enterprise prediction workflows for reducing downtime. Implemented Kafka-based streaming inference and integrated ML models with Hyperledger Fabric, with observability via Grafana dashboards.

Asset Tokenization ML Pipelines predictive modeling PyTorch scikit learn Kafka Streaming Inference Hyperledger Fabric Observability Grafana

Data Scientist

Levatas

Apr 2016 - Dec 2017 (1 year 8 months)

Developed churn prediction and customer lifetime value models to support retention strategies. Built AutoML pipelines, optimized feature extraction for faster experimentation, and deployed ML microservices into a Django backend for BI dashboards.