Kenneth Rizady Soriano

Designed and developed a full-stack engineering analytics platform. Ingested data from GitHub, Jira, and CI/CD systems to compute delivery metrics, detect workflow bottlenecks, and provide actionable insights via role-based dashboards. Backend & AI engineering built Django REST Framework-based services to manage financial workflows; Frontend developed React/TypeScript dashboards for 500+ users; DevOps implemented Docker-based deployments and CI/CD pipelines. Improved performance with asynchronous processing, internal and third-party data integration, and robust logging/monitoring for compliance.

Built large-scale data ingestion pipelines using Python, Apache Kafka, and PostgreSQL to process 50M+ engineering events daily from GitHub, Jira, and CI/CD platforms. Developed engineering analytics services in Django/PostgreSQL to automatically calculate DORA metrics, delivery forecasts, workload allocation, and engineering capacity across hundreds of teams. Designed semantic search and RAG systems using LangChain, Pinecone, FAISS, and OpenSearch for sub-second retrieval of engineering knowledge and operational insights. Built an AI Engineering Copilot using GPT-4, Claude, LangGraph, and FastAPI to help engineering leaders query delivery risks, team performance, and project status in natural language. Implemented asynchronous AI inference pipelines using Python, Celery, Redis, and Docker; developed interactive analytics dashboards with React/TypeScript and Material UI; optimized PostgreSQL performance to reduce complex analytic query times by 65%. Implemented enterprise authenticati

Built large-scale data ingestion pipelines using Python, Apache Kafka, and PostgreSQL to process 50M+ engineering events daily. Developed engineering analytics services with Django and PostgreSQL that automatically calculate DORA metrics, delivery forecasts, workload allocation, and engineering capacity across hundreds of teams. Designed semantic search and Retrieval-Augmented Generation (RAG) systems using LangChain, Pinecone, FAISS, and OpenSearch for sub-second retrieval of engineering knowledge and operational insights. Built an AI Engineering Copilot using GPT-4, Claude, LangGraph, and FastAPI to help engineering leaders query delivery risks, team performance, and project status in natural language. Implemented asynchronous AI inference pipelines using Celery, Redis, and Docker, improving response latency and supporting high-concurrency workloads. Optimized PostgreSQL performance via indexing, partitioning, and caching, reducing complex analytics query times by 65%. Implemented