Available to hire
I’m Arun Kumar, a Senior Generative AI & Machine Learning Engineer with 10+ years of hands-on experience designing, training, deploying, and monitoring AI/ML models across government, finance, and healthcare domains under HIPAA, GDPR, and PCI-DSS compliance frameworks. I specialize in building end-to-end ML pipelines leveraging Azure ML, AWS SageMaker, and Databricks, integrated with MLflow, Airflow, and Kubeflow to enable full MLOps automation, versioning, and continuous retraining across environments.
From medical imaging and regulatory AI to real-time fraud detection and NLP-powered knowledge tools, I partner with cross-functional teams to translate messy data into interpretable, auditable AI systems. I enjoy mentoring teams on ethical AI, governance, and Responsible AI practices, and I’m passionate about delivering impact with thoughtful, compliant AI solutions.
Skills
Work Experience
Senior AI Engineer at County of San Bernardino, CA
September 1, 2023 - PresentArchitected multi-modal and VLM-driven AI systems integrating medical imaging, sensor telemetry, and patient EHR data; implemented regulatory-grade data pipelines with Delta Lake and Azure Purview for HIPAA/GDPR audit readiness. Led generative AI initiatives (Azure OpenAI, LangChain) to automate regulatory documentation review and device reporting with human-in-the-loop validation. Developed edge AI, CV models (Swin Transformers, MONAI) for defect detection in medical devices; accelerated distributed training with Triton and vLLM; built risk stratification models with interpretable scores. Established model governance, QA pipelines, explainability dashboards, and privacy-preserving AI (federated learning, differential privacy) across cross-hospital deployments. Environment: Python, PyTorch Lightning, TensorFlow Extended, Azure ML, Databricks, Delta Lake, MONAI, OpenAI, LangChain, LangGraph, CrewAI, RAG, GPT-4, vLLM, TGI, MLflow, Azure DevOps, Triton Inference Server, TensorRT, ONNX, Li
AI Engineer at American Airlines, Fort Worth, TX
May 1, 2021 - July 1, 2023Designed, trained, and deployed AI/ML models for credit risk scoring and fraud detection; integrated with Azure ML pipelines ensuring PCI-DSS and SOX compliance. Built scalable data pipelines with Azure Data Factory and PySpark ingesting millions of transactions daily; implemented data quality rules aligned with GDPR and audit controls. Developed deep learning models for anomaly detection and fraud using autoencoders and RNN/LSTM; NLP models (HuggingFace Transformers, SpaCy, BERT) for sentiment/intent in KYC documents; automated MLflow-based CI/CD and MLOps. Implemented real-time streaming with Kafka/Spark Streaming and AWS Kinesis; deployed microservices with FastAPI/Flask; mentored teams on fairness and explainability (SHAP/LIME) and data governance. Collaborated on cybersecurity analytics for insider threat detection; established feature stores and metadata tracking in Azure ML/Databricks; led cross-functional MLOps automation.
ML Engineer at State Of Florida
March 1, 2018 - February 1, 2021Designed and deployed ML models for fraud detection and risk scoring across multiple state agencies; integrated with Azure ML pipelines and SQL Server back-ends with HIPAA and SOX compliance. Built scalable ETL data ingestion using PySpark, Pandas, and Azure Data Factory; performed feature engineering with governance. Implemented supervised/unsupervised models (Logistic Regression, Random Forest, Gradient Boosting, K-Means) using Scikit-learn and XGBoost; deployed REST APIs via Flask/FastAPI; integrated with Power BI/Tableau. Established model explainability (SHAP/LIME) and data quality validation (Great Expectations); containerized apps with Docker and CI/CD via Azure DevOps/Jenkins. Initiated bias/fairness testing and standardized ML lifecycle across Azure/AWS, including governance and documentation.
Data Scientist at Wells Fargo, Charlotte NC
May 1, 2016 - September 1, 2017Developed predictive models for shipment routing and logistics optimization; validated and refined features to improve operational efficiency. Built end-to-end ML pipelines with Airflow and SQL, performing real-time analytics with IoT/GPS data. Implemented anomaly detection, time-series forecasting (ARIMA/Prophet/LSTM), and clustering (K-Means) to support decision-making. Exposed models as REST endpoints, integrated with dashboards in Power BI/Tableau, and conducted A/B testing to validate improvements. Emphasized data governance and reproducibility with Git-based versioning and CI/CD pipelines.
Junior Data Engineer at Siemens, India
February 1, 2014 - January 1, 2016Designed and maintained robust ETL pipelines using Python, SQL, and Airflow; optimized workflows to reduce processing time by 20%. Built data models and warehouse solutions in SQL Server and Snowflake for manufacturing analytics. Ingested and transformed large-scale IoT datasets with PySpark, Spark, and Talend; monitored pipelines with Kafka/Spark Streaming. Implemented data validation and cleansing routines; containerized applications with Docker; supported cloud and on-prem storage (AWS S3, Azure Blob). Documented data workflows and governed data pipelines following enterprise standards.
Education
Qualifications
Industry Experience
Government, Financial Services, Healthcare, Manufacturing, Software & Internet
Skills
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