As a Generative AI Data Scientist at Cloud Computing Technologies (2000–Present), I lead the development and deployment of scalable machine learning models and generative AI solutions within secure cloud-native environments. My work spans end-to-end AI lifecycle management, from data ingestion and training pipelines to deploying custom LLMs using Amazon Bedrock, AWS SageMaker, Databricks, and other AWS-native services. I specialize in building real-time inference systems, fine-tuning foundation models, and integrating AI into enterprise-grade applications for industries such as healthcare, finance, and cybersecurity.
Leveraging advanced natural language processing (NLP) and transformer architectures, I design AI-driven systems that power intelligent automation, chatbots, document summarization, and insight generation. I have successfully led projects that implement generative AI workflows using tools like LangChain, Pydantic AI, Vector Databases, and serverless architectures on AWS Lambda. My expertise includes optimizing AI pipelines for cost efficiency, performance, and regulatory compliance, including secure data handling in FedRAMP and HIPAA-sensitive environments.
In addition, I bring advanced experience with Databricks, having architected and deployed a secure, FedRAMP Moderate-compliant Databricks E2 platform on AWS. My work includes implementing Databricks MLOps pipelines, Infrastructure-as-Code using Terraform, and integrating Delta Lake, Unity Catalog, and Databricks Feature Store to support scalable, compliant AI/ML workflows. I have developed AI-powered analytics solutions for healthcare using TensorFlow, Python, and Spark R, while ensuring end-to-end security monitoring with Splunk and maintaining FISMA and NIST compliance standards.
In my current role, I also actively collaborate with DevOps, data engineering, and cloud security teams to integrate AI/ML models into production environments. I lead efforts in optimizing performance, ensuring regulatory compliance, and embedding AI-driven functionality across platforms. My responsibilities also include mentoring team members and staying ahead of advancements in foundation models, prompt engineering, Pydantic for data validation, and autonomous AI agents that power scalable, cloud-native solutions.
Leveraging advanced natural language processing (NLP) and transformer architectures, I design AI-driven systems that power intelligent automation, chatbots, document summarization, and insight generation. I have successfully led projects that implement generative AI workflows using tools like LangChain, Pydantic AI, Vector Databases, and serverless architectures on AWS Lambda. My expertise includes optimizing AI pipelines for cost efficiency, performance, and regulatory compliance, including secure data handling in FedRAMP and HIPAA-sensitive environments.
In addition, I bring advanced experience with Databricks, having architected and deployed a secure, FedRAMP Moderate-compliant Databricks E2 platform on AWS. My work includes implementing Databricks MLOps pipelines, Infrastructure-as-Code using Terraform, and integrating Delta Lake, Unity Catalog, and Databricks Feature Store to support scalable, compliant AI/ML workflows. I have developed AI-powered analytics solutions for healthcare using TensorFlow, Python, and Spark R, while ensuring end-to-end security monitoring with Splunk and maintaining FISMA and NIST compliance standards.
In my current role, I also actively collaborate with DevOps, data engineering, and cloud security teams to integrate AI/ML models into production environments. I lead efforts in optimizing performance, ensuring regulatory compliance, and embedding AI-driven functionality across platforms. My responsibilities also include mentoring team members and staying ahead of advancements in foundation models, prompt engineering, Pydantic for data validation, and autonomous AI agents that power scalable, cloud-native solutions.
