Kerry Dor

About

I’m a full‑stack engineer specializing in building production‑ready AI systems that combine strong software engineering with practical machine intelligence. My work spans end to end product development from designing clean, scalable backend architectures to shipping polished frontend experiences with a particular focus on integrating LLMs, automation, and intelligent workflows into real applications. I’ve built systems that process large, fast moving data streams, orchestrate multi agent AI pipelines, and automate complex business operations with reliability and low latency. I enjoy roles where I can move between architecture, implementation, and iteration, turning ambiguous ideas into working software that feels fast, stable, and thoughtfully engineered. Whether I’m building internal tools, customer facing products, or AI‑powered automation, I care about clarity, maintainability, and delivering features that actually move the needle for the business.

Experience

• Senior AI & Fullstack Engineer · Kanda Software04-01-2023 – 05-01-2024

  Designed and built backend systems, distributed pipelines, and intelligent automation powering pricing, invoicing, and customer usage analytics across digital banking products. • Engineered a usage based billing platform that ingests high volume API/session activity, processes it through PySpark on EMR, and exposes structured datasets for downstream AI and analytics workflows. • Designed backend architecture across Kinesis, Firehose, S3, Airflow, and Step Functions, implementing rating, proration, and invoice generation logic as modular, fault tolerant services. • Designed a fraud scoring feature store serving engineered signals such as device velocity metrics, geolocation mismatch flags, and behavioral patterns using S3 + Glue Catalog. Standardized feature computation and storage to significantly reduce model training data preparation time. • Built recovery and validation workflows using SQS, Lambda, and DynamoDB to ensure correctness, prevent double billing, and provide operational transparency through automated reconciliation alerts. • Developed a Redshift billing warehouse with optimized schema patterns to support real time dashboards, reporting, and data driven product insights. Fraud, Risk & Decisioning Feature Platform — Lending & Financial Operations Built AI ready feature pipelines and data services supporting fraud detection, onboarding, and decisioning systems. • Designed a feature store that standardizes engineered signals such as device velocity patterns, behavioral indicators, and geolocation mismatches using S3 + Glue Catalog. • Streamlined feature computation and retrieval to accelerate model iteration and ensure consistent inputs for AI driven scoring and decisioning workflows. Environments: Node.js, TypeScript, Python, PySpark, React/Next.js, AWS (Kinesis, Firehose, S3, EMR, Glue, Athena, Redshift, Lambda, Step Functions, SQS, SNS), Airflow, Docker, Terraform, Git, Postgres, Redis, Parquet, JSON

• Senior Fullstack Developer · HatchWorks01-01-2022 – 02-01-2023

  Internal platforms supporting mortgage application processing, underwriting workflows, pricing evaluation, borrower-journey insights, and operational reporting. • Developed and maintained backend services and ETL components that transformed complex loan-application JSON into normalized, analytics-ready datasets, implementing PySpark logic to flatten nested borrower, property, and income structures while handling malformed records. • Built service integrations that linked application, underwriting, and closing events across multiple systems using deterministic identity logic, enabling consistent borrower-journey tracking and funnel analysis. • Extended pricing and evaluation services to support new lending products by updating schemas, enriching backend logic, and backfilling historical data for downstream systems and analytics teams. • Optimized key Athena workloads by restructuring queries, improving partition pruning, and tuning Parquet layout to improve reporting performance during peak cycles. • Refactored Spark jobs suffering from skewed joins by introducing key-salting and broadcasting small lookup tables, stabilizing memory usage and improving job reliability across data-processing pipelines. Environments: Python, PySpark, Node.js, SQL, AWS (S3, EMR, Glue, Athena), Airflow, Terraform, Docker, Git, Iceberg, Parquet, JSON

• Junior AI Developer | Former Intern · Andersen Lab03-01-2019 – 11-01-2021

  Financial Data Automation & Intelligence Systems — Internal Finance Platforms Built automation scripts, backend services, and data processing workflows that supported financial reporting, reconciliation, and operational analytics across internal finance applications. • Automated a monthly financial reconciliation workflow by converting manual SQL queries and Python data prep steps into a scheduled job, enabling consistent, repeatable data processing for downstream analytics and model ready datasets. • Replaced a legacy CSV based data exchange between two internal finance systems with a JSON based REST API, improving data quality, enabling structured validation, and laying groundwork for future AI driven data checks. • Diagnosed and resolved a production pipeline issue caused by late arriving reference data; added freshness checks and automated alerts to ensure upstream data reliability for downstream analytical and ML ready processes. • Enhanced deployment automation (Jenkins + shell scripts) by adding unit tests for SQL transformation logic, improving reliability of data pipelines used for reporting and feature generation. Environments: Python, SQL Server, REST APIs, AWS, Azure, Jenkins, Git/GitHub, Bash, JSON

Education

• Boston UniversityBachelor's Degree, Computer Science

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