Open to opportunities

Mohammad Hamza

@mohammadhamza2

Message

I build scalable LLM, RAG, and MLOps platforms for enterprise AI.

United States

Message

What I'm looking for

I’m looking for remote opportunities where I can lead scalable generative AI, RAG, MLOps, and multi-cloud platform initiatives, partner across product and compliance teams, and turn complex business challenges into measurable outcomes.

At Pixelette Technologies, I lead enterprise AI strategy and productionize LLM, multimodal, and generative AI systems across healthcare and enterprise teams. I’ve built RAG pipelines with FAISS, Pinecone, and hybrid search that reduced hallucinations by 40%, and optimized models with LoRA, DeepSpeed, ONNX, and TensorRT to reduce costs by 30%.

Previously at MindsDB, Feedzai, and Uniphore, I built real-time inference, fraud detection, forecasting, voice-intent, and data platforms handling millions of daily transactions. My work spans Kubernetes, AWS, Azure, GCP, Databricks, PySpark, Airflow, MLflow, Kubeflow, and responsible AI controls for regulated environments.

I enjoy translating ambiguous business problems into scalable AI products, from self-service MLOps portals and compliance intelligence assistants to multi-agent workflow systems with human-in-the-loop controls.

Experience

Work history, roles, and key accomplishments

Current

Lead AI/ML Engineer

Current

Pixelette Technologies Ltd

Mar 2022 - Present (4 years 5 months)

Directed enterprise AI strategy and operationalized LLMs, multimodal models, and generative AI systems in production across healthcare and enterprise business units. Architected RAG pipelines using FAISS, Pinecone, and hybrid search, reducing hallucinations by 40% and improving retrieval accuracy.

LLM Generative AI RAG Faiss Pinecone Kubernetes Azure ML Databricks Sagemaker DeepSpeed Onnx TensorRT RBAC Audit Logging Usage Metering Billing Prometheus Grafana OpenTelemetry Langsmith Responsible AI Lineage

Senior MLOps Engineer

MindsDB

Feb 2019 - Feb 2022 (3 years)

Built scalable ETL and feature pipelines using PySpark and Databricks across AWS and GCP for multi-terabyte datasets. Improved model performance by 15–22% via feature engineering, hyperparameter tuning, and evaluation.

PySpark Databricks AWS GCP ETL Feature Engineering Hyperparameter Tuning Microservices Kubernetes Ray Kubeflow MLFlow GitHub Actions SHAP LIME

Sr. Data Scientist (AI & Forecasting)

Feedzai

Dec 2016 - Jan 2019 (2 years 1 month)

Engineered transformation pipelines with PySpark and Airflow for behavioral and transaction data. Built forecasting, segmentation, optimization, and anomaly detection models, improving planning accuracy by 20–30%.

PySpark Airflow Forecasting Segmentation Optimization Anomaly Detection A B Testing Causal Inference Azure Data Factory Transformer Models RAG Fraud Detection

Machine Learning Engineer

Uniphore

Feb 2015 - Nov 2016 (1 year 9 months)

Built foundational ML and data pipelines for voice-intent recognition systems in enterprise customer engagement. Designed ingestion and preprocessing workflows using Azure Data Factory, Event Hubs, Airflow, and SQL.