Segun Bakare

– Architected and implemented end-to-end document ingestion pipelines processing 10M+ documents monthly, transforming unstructured data into Neo4j knowledge graphs with 99.5% accuracy for the USAID IPs data – Built and optimized Python/FastAPI microservices exposing RESTful ingestion and Q&A endpoints, achieving sub-200ms response times at 10K+ RPS – Reduced operational costs by 40% through intelligent caching strategies, query optimization, and efficient vector similarity search implementations for both HIV and TB USAID partners. – Developed conflict resolution algorithms for handling contradictory information sources, implementing temporal versioning and confidence scoring systems – Collaborated with ML research team to transition experimental retrieval-augmented generation (RAG) models into production, reducing inference latency by 60% for the LAMISPlus used by the USAID IPs – Established comprehensive testing framework with 95% code coverage, automated CI/CD pipelines, and real-time observability dashboards

– Designed and deployed scalable document processing pipelines using Apache Airflow and Kafka, handling 1M+ documents daily – Implemented retrieval optimization strategies including semantic chunking, hybrid search (dense + sparse), and query expansion, improving answer relevance by 35% – Built production ML models for named entity recognition, relationship extraction, and document classification using BERT and custom transformer architectures – Scaled prototype systems from handling 1K to 1M+ documents through distributed processing, containerization, and auto-scaling infrastructure – Maintained 95.9% service uptime through comprehensive monitoring, alerting, and automated failure recovery mechanisms

– Developed graph database schemas and optimized Cypher queries for complex relationship traversals across 2M+ node networks – Created Python libraries for graph data validation, schema evolution, and automated data quality checks – Integrated multiple data sources including APIs, databases, and file systems into unified knowledge representation – Implemented A/B testing framework for evaluating different graph construction and retrieval strategies

– Designed data pipeline architectures on AWS, ensuring scalability for high-volume daily access by utilizing a suite of technologies including DBT, Apache Airflow, and Amazon Redshift. – Constructed real-time data pipelines with Databricks and Apache Kafka, effectively reducing data latency by 37%. – Implemented ETL processes that enhanced data accessibility and reporting efficiency for key data repositories. – Developed data quality frameworks and collaborated on optimizing data models for advanced analytics and machine learning projects for the GoN and TB USAID implementing partners