Senior Software Engineer with 5+ years of experience building backend services, data platforms, APIs, cloud applications, and applied AI solutions.
Previously, I worked as a Senior Software Developer at QuinStreet, where I built and supported production systems using Java, Spring Boot, Python, SQL, AWS, Apache Airflow, React, and Vue.
I am currently exploring Senior Software Engineer opportunities across backend engineering, full-stack development, data platforms, cloud systems, and Generative AI.
- Backend services and REST APIs
- Java and Spring Boot applications
- Python automation and integrations
- Data platforms and ETL pipelines
- Apache Airflow workflow orchestration
- Database design and SQL performance optimization
- AWS serverless and cloud-native applications
- Generative AI, RAG, MCP, and knowledge-base integrations
- Production monitoring, incident investigation, and root-cause analysis
- Built Java, Spring Boot, and Python systems supporting reporting, billing, forecasting, and revenue operations
- Developed reusable Python integrations for more than 20 client systems
- Designed Apache Airflow workflows with validation, retries, backfills, monitoring, and failure recovery
- Improved dashboard response times to approximately 2–3 seconds through schema redesign, indexing, and SQL optimization
- Reduced reporting workflows to under 60 minutes through database and pipeline improvements
- Delivered integrations associated with an estimated $2–2.5 million in quarterly revenue
- Designed AWS infrastructure using Terraform for an AI live-avatar solution powered by Amazon Bedrock, RAG, MCP, and enterprise knowledge bases
An MCP-powered RAG assistant for engineering incident investigation.
OpsLens AI searches engineering runbooks and historical incidents using semantic retrieval, filters weak matches, and generates evidence-grounded troubleshooting guidance with sources, confidence, and limitations.
- Retrieval-Augmented Generation
- Model Context Protocol server and client integration
- Semantic search using ChromaDB
- Runbook and historical-incident retrieval
- Vector-distance relevance filtering
- Evidence-grounded LLM generation
- Unsupported-query detection
- Retrieval evaluation and testing
- Streamlit application development
Technology: Python, Streamlit, MCP, ChromaDB, OpenAI, RAG, semantic search
Java Python SQL TypeScript JavaScript PHP Shell
Spring Boot Spring Framework Flask REST APIs Backend Services API Integrations
Apache Airflow ETL Pipelines Data Ingestion Data Validation Incremental Loads Backfills Failure Recovery
MySQL MongoDB DynamoDB Schema Design Relational Modeling Indexing Query Optimization
AWS Lambda API Gateway IAM S3 Terraform Docker Linux Git CI/CD
React Vue TypeScript JavaScript HTML CSS
Amazon Bedrock RAG MCP Knowledge Bases Embeddings Semantic Search Prompt Engineering
- Scalable backend architecture
- Data-intensive applications
- Cloud-native engineering
- Workflow orchestration
- Database performance
- AI-assisted engineering tools
- Retrieval-Augmented Generation
- Production reliability and observability
I am currently focused on:
- strengthening GenAI and AI-system design skills
- building practical RAG and MCP applications
- expanding backend and cloud architecture knowledge
- preparing for senior-level system design and coding interviews
- developing production-oriented portfolio projects