Reid Long

· Designed and developed AI-powered customer intelligence features across a platform supporting 70+ enterprise data integrations including Zendesk, Intercom, Qualtrics, Salesforce, Snowflake, S3, and custom APIs.

· Built scalable Python ingestion services that normalized millions of customer feedback records into a unified data model while supporting incremental synchronization, deduplication, and PII redaction.

· Developed backend services with FastAPI and GraphQL powering customer analytics, semantic search, reporting dashboards, and AI-assisted workflows.

· Built production Retrieval-Augmented Generation (RAG) pipelines using Pinecone vector search and hybrid retrieval to provide evidence-backed responses from customer feedback.

· Designed and implemented multi-step AI agent workflows using LangGraph and LangChain with tool calling, planning, memory management, and structured output generation.

· Integrated OpenAI GPT-4 and Anthropic Claude models for conversational analytics, automated customer insight generation, and product feedback summarization.

· Built streaming AI experiences enabling real-time responses through React and TypeScript while supporting conversation persistence and dynamic visualization components.

· Developed AWS Step Functions and AWS Batch pipelines orchestrating large-scale NLP processing including sentiment analysis, clustering, topic extraction, taxonomy generation, and semantic indexing.

· Improved AI quality by implementing automated evaluation pipelines, prompt optimization, hallucination reduction strategies, and model routing based on workload characteristics.

· Mentored engineers on production AI architecture, scalable backend development, and LLM engineering best practices.

· Developed backend services supporting clinical conversation processing and AI-powered documentation generation for healthcare providers.

· Built secure REST APIs enabling physician workflows, medical transcript processing, and structured clinical note generation.

· Collaborated closely with applied machine learning engineers to productionize speech recognition and NLP models into scalable cloud services.

· Designed distributed processing pipelines handling large volumes of medical conversations while maintaining HIPAA-compliant data handling practices.

· Improved application performance through Redis caching, asynchronous task processing, database optimization, and event-driven architecture using Kafka.

· Built React-based clinician interfaces supporting transcript review, AI-generated documentation, and workflow automation.

· Containerized services using Docker and Kubernetes while improving deployment reliability through CI/CD automation on AWS.

· Worked cross-functionally with clinical specialists, product managers, and ML researchers to improve documentation accuracy and physician productivity.

· Promoted to Senior Software Engineer after consistently delivering high-impact platform improvements and mentoring junior engineers.

· Developed backend functionality for large-scale distributed services within Google's internal engineering infrastructure.

· Built automation tools that improved developer workflows and reduced manual testing effort.

· Optimized backend components through profiling, performance tuning, and comprehensive unit testing.

· Collaborated with senior engineers in a large-scale monorepo environment following Google's engineering standards, design reviews, and code review practices.

· Presented project results and technical recommendations to engineering leadership upon internship completion.