Artificial Intelligence

• Define integration strategies for connecting AI models, data warehouses, and business applications.
• Develop architectural blueprints for modular, API-driven, and event-based integrations.
• Ensure compliance with enterprise security, governance, and performance standards.
• Design workflows for ingesting, transforming, and serving data to AI models.
• Implement integration patterns for real-time and batch AI inference.
• Optimize data pipelines for latency, throughput, and cost efficiency.
• Evaluate and recommend integration tools and platforms (e.g., Azure Data Factory, Databricks, API Gateways).
• Establish best practices for AI model deployment and monitoring within integrated environments.
• Drive adoption of MLOps and DataOps principles across teams.
• Partner with data engineers, ML engineers, and business analysts to align integration solutions with business needs.
• Communicate architectural decisions and trade-offs to technical and non-technical stakeholders.
• Provide technical guidance to development teams during implementation.
• Ensure integrations adhere to data privacy, security, and regulatory requirements.
• Implement monitoring and alerting for integration health and AI model performance.

AI & Data Integration Architect

Patent Engineer – AI/ML, Kernel & High-Performance Compute

Patent Engineer – AI/ML, Kernel & High-Performance Compute

AI Catalyst

Senior Director Analyst, Healthcare Technology and AI Markets

AI Intern