Cassio – Python, AI agent development, AWS

It's an AI automation platform for healthcare operations.

• Designed and shipped AI-powered workflow automations for healthcare operations.
• Built back-end services and integrations connecting LLM agents, APIs, and customer workflows.
• Collaborated with clients, product, and engineering to define requirements and deliver production solutions.
• Improved operational efficiency by automating repetitive manual healthcare processes.

It's an event-driven exception management platform for alternative investment operations. The system replaces slow manual triage workflows with Kafka-based event processing, dynamic rule queues, and analyst-facing prioritization tools.

• Built FastAPI services and Kafka pipelines for scalable exception processing.
• Designed dynamic rule-based queues to prioritize and route operational exceptions.
• Integrated AWS Lambda consumers with PostgreSQL persistence.
• Added Datadog observability and Terraform infrastructure automation.
• Reduced average exception handling time from 15 minutes to 90 seconds.

It's a Databricks ETL optimization for enterprise data pipelines (a multinational fast-food holding company).

• Optimized Databricks ETL jobs, reducing runtime by more than 70%.
• Rebuilt and tested AWS Glue jobs to improve reliability.
• Improved Spark-based data workflows for scalability and maintainability.
• Supported Python and scikit-learn data processing workflows.

It's a personalized learning platform serving language learners across multiple modalities.

• Led a small engineering team delivering personalized learning features.
• Built back-end APIs and workflows using FastAPI and PostgreSQL.
• Collaborated with product stakeholders to translate learning goals into technical solutions.
• Improved AWS infrastructure and CI/CD processes, reducing operational costs.

It's a research infrastructure data platform used by universities and research institutions.

• Migrated legacy Pentaho ETL workflows to Apache Airflow.
• Built and maintained data pipelines for academic and research infrastructure.
• Improved search and indexing workflows using Elasticsearch.
• Added monitoring and observability with Grafana and MongoDB-backed systems.