Murad K.-Senior AI/ML Engineer

• Led end-to-end delivery of ML/AI applications for the QuantistryLab simulation platform, from requirements definition and solution design to development, testing, deployment, and production support, coordinating efforts across back-end, DevOps, and ML teams
• Designed and implemented a compound search system with an open-weights LLM, the Pydantic AI agent framework, and the Model Context Protocol (MCP), exposing it through FastAPI endpoints
• Deployed and configured vLLM-based LLM serving on Kubernetes
• Built CI/CD pipelines and Helm charts to automate deployments across staging and production environments
• Instrumented the search system with OpenTelemetry spans and traces, integrating with the Honeycomb observability platform to monitor component-level latency and functional errors
• Developed a custom evaluation dataset and implemented evaluations for retrieval relevance, answer correctness, tool-call accuracy, and end-to-end task success
• Reduced infrared spectrum prediction time by 83% (from 60 to 10 minutes) by replacing a quantum-mechanical model with a pretrained graph neural network; fine-tuned the model using PyTorch, built and containerized the inference pipeline with Docker, and integrated it into the platform
• Implemented an MLflow model registry with MinIO-backed artifact storage
• Advised sales and science teams on ML/AI applications for prospective client projects, providing technical expertise to shape proposed solutions and prototypes

Technologies: AWS, Hetzner, Kubernetes, Helm, Docker, vLLM, Pydantic AI, MCP, Qwen, Llama 3, MLflow, MinIO, GitHub Actions, Python, FastAPI, OpenTelemetry, Honeycomb, PyTorch, Polars, pytest

• Automated income and expense verification for loan applications by designing and deploying ML models on Google Cloud Platform (GCP), using PySpark for data processing, Airflow for orchestration, and Azure Pipelines for CI/CD, eliminating manual verification for up to 70% of applications
• Developed Random Forest and XGBoost classifiers for transaction classification, app review classification, and KYC name screening, including data cleaning, feature engineering, and cross-validation
• Addressed class imbalance through cost-sensitive training, oversampling/undersampling, and decision-threshold optimization to balance precision and recall
• Collaborated with product owners, end-users, and engineering teams to gather requirements, design solutions, and deliver models, data visualizations, and interactive dashboards
• Conducted workshops on building ML pipelines with Kedro and promoted its adoption across the department

Technologies: Python, PySpark, GCP, Airflow, BigQuery, Azure DevOps, scikit-learn, XGBoost, pandas, numpy, seaborn, Matplotlib, Dash, pytest