I build production-grade ML systems, data pipelines and cloud-native applications โ from RAG search over NASA catalogs to automated trading platforms.
Full-Stack and Machine Learning Engineer with 7+ years of experience shipping production web platforms and AI systems across NASA-funded research, e-commerce, and fintech. Expert in Python (Django, FastAPI), React/TypeScript, and AWS, with deep hands-on experience in LLM applications, RAG pipelines, LangGraph agent orchestration, vector databases, and parameter-efficient fine-tuning (LoRA/DoRA). Published researcher (70+ citations) completing an M.S. in Computer Science. Proven quantifiable impact: 30% faster data delivery at NASA, 35% lower API latency at Houzz, 25% infrastructure cost reduction via Kubernetes autoscaling.
Experience
Where I've worked
5+ years building production ML systems, data pipelines and cloud-native platforms.
Research Engineer (ML & Backend) | Part-Time ยท Huntsville, AL
Aug 2024 โ Present
Drove a 30% increase in data delivery speed to clients through the implementation of the new Tasking Management System, significantly improving data ingestion and request handling for the NASA CSDA program.
Initiated and built an agent-based data search project leveraging LLMs with Retrieval-Augmented Generation (RAG) using LangChain and LangGraph, enabling users to intelligently search and retrieve specific data from a massive STAC metadata catalog.
Developed a RAG-based automatic metadata file generator for the CSDA program, utilizing local LLM models with Weaviate and ChromaDB as vector databases and maintaining a knowledge graph for NASA Spatiotemporal Asset Catalog compliance.
Managed scalable infrastructure across AWS (S3, EC2, RDS, Lambda, Step Functions) to ensure a robust and highly available system using technologies including Next.js and FastAPI.
Implemented data ingestion pipelines for new vendors, including Satellogic and Maxar DEM, within the CSDA system.
Developed and pioneered the new NASA CSDA tasking management system, utilizing a Django/Airflow backend and a React and TypeScript frontend to manage commercial satellite data requests and ingestion.
Boosted website performance by 20% by leading the migration of a legacy PHP-based system to a modern Python/Django backend with Server-Side Rendered (SSR) architecture, reducing API response times by 35%.
Developed an automated background removal pipeline for importing product images from external sources, leveraging OpenCV and deep learning models (U-Net) to process 5,000+ images daily with 95% accuracy.
Architected and managed heavy AWS infrastructure including EC2, ECS, S3, RDS (PostgreSQL), and CloudFront, ensuring 99.9% uptime across production services.
Implemented automated CI/CD pipelines using Jenkins with blue-green deployment strategies, reducing deployment downtime by 90% and enabling zero-downtime releases.
Configured KEDA (Kubernetes Event-Driven Autoscaling) for dynamic workload scaling, reducing infrastructure costs by 25% during off-peak hours.
Managed infrastructure as code using Ansible and Terraform, provisioning and maintaining Amazon RDS instances, load balancers, and auto-scaling groups across multiple environments.
Developed a quantitative analytical trading platform while strictly adhering to Agile methodologies, ensuring timely and high-quality deliverables.
Integrated Python with Django and the Django REST Framework for reliable backend services, managing SQL databases and emphasizing cross-functional collaboration.
Built responsive, intuitive interfaces using React.js on the frontend, integrating Plotly.js for data visualization and Context APIs for robust state management.
Streamlined platform releases by implementing Git version control and CI/CD pipelines to enable smooth deployments to AWS, including Lambda functions for event-driven processes.
Implemented comprehensive observability using Prometheus + Grafana for metrics, Loki + Grafana for log aggregation, and AWS CloudWatch for infrastructure monitoring across the entire trading platform.
Utilized Django, React.js, Plotly, and scikit-learn to enhance platform functionality, resulting in improved data processing and visualization capabilities.
Led the backend development of the ListInfo hotel booking platform using Python, Django, and Django REST Framework, architecting RESTful APIs that served 100K+ daily requests.
Deployed and managed the full application stack on AWS using EC2, ALB, ELB, and ECR for containerized deployments, achieving 99% uptime.
Designed and implemented CI/CD pipelines for automated testing, building, and deploying Docker containers to AWS ECS, reducing release cycles by 50%.
Optimized PostgreSQL database performance through query tuning, indexing strategies, and connection pooling, resulting in a 40% reduction in average query execution time.
AI-powered Grafana app plugin that generates dashboards from natural-language prompts.
Built an AI-powered Grafana app plugin that generates complex dashboards from natural-language prompts, eliminating hand-authored panel JSON; architected as a React/TypeScript frontend, secure Go proxy protecting API credentials, and a headless Python/FastAPI agent. Orchestrated multi-step LLM workflows with LangGraph and PydanticAI, using Model Context Protocol (MCP) servers to autonomously discover OpenSearch data sources and write dashboards directly to Grafana. Engineered low-latency WebSocket token streaming to the browser plus a database-backed config layer with hot-swappable LLM providers (OpenAI, Anthropic, Ollama) and PostgreSQL-persisted conversation history.
Conversational agent letting scientists search NASA Earth-observation datasets in plain English.
Built a conversational agent (FastAPI, Chainlit, LangGraph, PydanticAI) that lets scientists search and download NASA Earth-observation datasets in plain English, using local LLMs and embeddings via Ollama to translate ambiguous prompts into precise STAC search filters. Designed a dual-database retrieval architecture โ PostgreSQL + pgvector for auth and conversational state recall, Neo4j for relationship-aware semantic catalog retrieval โ with a real-time Mapbox panel over WebSockets visualizing spatial queries and dataset heatmaps. Containerized the backend with Docker Compose, integrating NASA Earthdata OAuth for secure asset downloads and Logfire for end-to-end agent observability.
Time-series pipeline forecasting short-horizon price movement with stacked LSTM networks.
Automated slow, emotion-driven manual trading by engineering a time-series pipeline (Python, TensorFlow/Keras) that cleans OHLCV market history, derives technical-indicator features, and trains stacked LSTM networks to forecast short-horizon price movement. Implemented a rule-based execution layer converting model forecasts into buy/sell/hold signals, backtested against held-out historical data before deployment.
LoRA-fine-tuned Gemma model generating stylistically coherent Nepali verse.
Curated and preprocessed a corpus of historic Nepali poems (Devanagari script), then fine-tuned Google Gemma with Unsloth using parameter-efficient LoRA adapters on a single consumer GPU. Produced a model generating stylistically coherent Nepali verse from short prompts, demonstrating practical low-resource-language adaptation of open-weight LLMs.
Mitigating Reconstruction Loss in Neural Compression for Remote Sensing Downstream Tasks Using Weight-Decomposed Low-Rank Adaptation
M.S. Thesis, 2026
Applied DoRA adapters to a neural codec's synthesis transform, no retraining, no bitstream change, via label-free distillation from a segmentation teacher, improving downstream consistency mIoU from 0.643 to 0.723 (+8.04 pp) at unchanged PSNR/MS-SSIM, outperforming JPEG, JPEG 2000, and WebP at low bitrates.
Comparative Analysis of Multiple Deep CNN Models for Waste Classification
arXiv, 2020
Benchmarked deep CNN architectures for automated waste sorting on TrashNet plus a self-collected dataset; fine-tuned ResNet-18 to 87.8% validation accuracy and deployed it in a smart dustbin prototype that physically segregates waste.