Senior Software Engineer (Data & AI Platform Engineering, LLM/LAG Processing) at Amazon Global Robotics

Architected a cloud-native AI decision-support platform on AWS using RAG and LangGraph multi-agent workflows, achieving 99.2% autonomous execution success. Built internal full-stack tools (React + FastAPI) for AI workflow management and monitoring, supporting 3,200 concurrent requests with 99.7% uptime. Implemented CI/CD and Kubernetes deployment strategies for AI microservices, improving production reliability. Designed distributed telemetry pipelines with Apache Spark and Airflow on AWS, ingesting 18M+ events daily and reducing latency from 6 hours to 12 minutes. Reduced hallucination rates from 12% to 2.1% using evaluation-based testing with LangSmith and custom pipelines. Mentored junior engineers and data scientists; provided advisory and roadmap support to stakeholders.

Senior Data Engineer (Data Infrastructure) at Amazon Global Robotics

Led IT consulting-style data engineering for enterprise data platforms by gathering requirements and translating them into cloud architectures and technical roadmaps. Architected AWS data infrastructure with Kafka and Apache Spark, reducing ingestion latency by 73%. Built and maintained Python-based ETL/ELT data models and automated testing frameworks. Integrated eight distributed data sources into a unified AWS data lake using Airflow, Python, and Golang, eliminating manual reconciliation and reducing SLA violations by 92%. Developed Java/Spring Boot REST APIs and full-stack integrations to automate bidirectional data movement, saving 40 hours weekly. Built streaming/ingestion acceleration for LLM research using Python distributed GPU optimization with PyTorch and Horovod, reducing training time by 95%. Mentored team members and advised stakeholders on scalable system design.

Senior Data Engineer at Audible

Provided client-facing data engineering consulting for product, content, and analytics teams, converting business requirements into technical roadmaps and scalable architectures. Built full-stack internal platforms using React, FastAPI, and MongoDB to streamline content metadata review and enrichment workflows. Developed Python backend services with Django and Flask for recommendation, subscription, and analytics APIs at high event volumes. Engineered AWS and Azure ingestion and transformation pipelines using Event Hubs, Data Factory, Databricks, Synapse, and SQL data marts. Implemented real-time streaming analytics with Databricks Structured Streaming and Python to monitor platform performance. Established CI/CD, containerization (Docker/Kubernetes), monitoring, observability, and data quality frameworks to enable reliable deployments. Mentored junior engineers on Python, distributed computing, and agile delivery practices.