Benedict Busole

Built production AI-assisted financial workflows using OpenAI and Claude APIs for transaction categorization, anomaly detection, documentation intelligence, and recommendation-style use cases. Designed provider abstractions, prompt templates, structured output schemas, validation and fallback paths, hallucination/format checks, caching, and auditable request logs. Developed Pytest/Jest evaluation and benchmarking harnesses to compare prompts, providers, model releases, and failure modes prior to production rollout. Implemented asynchronous Python/FastAPI and AWS Lambda/SQS workflows with retries, idempotency, PostgreSQL, Redis, Docker, CloudWatch, and production monitoring. Translated ambiguous requirements into tool/API contracts, orchestration logic, acceptance criteria, technical documentation, and reliable human-review paths.

Designed analytics-ready PostgreSQL models and transformation logic with reproducible request records, data-quality controls, and feature signals for financial decision workflows. Improved fraud-flag accuracy by 22% through refined normalized transaction data, validation rules, explainable signals, and downstream review workflows. Built Python-based event/data pipelines using AWS Lambda, SQS, PostgreSQL, S3, retries, idempotency, and operational monitoring. Integrated OpenAI and Claude APIs into categorization and anomaly-detection workflows, producing auditable structured outputs for downstream systems. Partnered cross-functionally to define source-of-truth logic, API contracts, data models, acceptance criteria, and reporting needs. Developed automated data and model-output checks with Pytest/Jest, raising automated coverage from 42% to 87%.

Designed ETL/ELT-style ingestion, normalization, validation, reporting endpoints, analytics-ready SQL views, audit logs, and recovery paths across e-commerce, healthcare, logistics, and operations data. Integrated 10+ external data sources by reverse-engineering schemas, handling authentication, inconsistent payloads, failure states, and data-quality edge cases. Optimized PostgreSQL and MySQL schemas, indexes, query plans, and access patterns, reducing average query execution time by 40%. Translated ambiguous business questions into reusable data models, metrics-ready views, documentation, and maintainable implementation plans.