Open to opportunities

Hi, I'm Ashish.

Full-Stack & Machine Learning Engineer

I build production-grade ML systems, data pipelines and cloud-native applications โ€” from RAG search over NASA catalogs to automated trading platforms.

Ashish Gautam
7+
Years experience
30%
Faster data delivery (NASA)
35%
Lower API latency (Houzz)
25%
Lower infra cost (K8s autoscaling)

About

Engineering across AI, data & cloud

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.

NASA IMPACT logo
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.
Project: Data Acquisition Request SystemAbout NASA IMPACT
Houzz logo
Software Engineer (ML & Backend) | Remote ยท Remote (Palo Alto, CA)
Apr 2023 โ€” Jul 2024
  • 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.
Project: Houzz MoodboardAbout Houzz
Software Engineer (ML & Backend) | Remote ยท Remote (Seattle, WA)
May 2021 โ€” Feb 2023
  • 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.
Project: Trading PlatformAbout Event Horizon Partners
Diagonal Software logo
Software Engineer (Full-Stack Development) | Remote ยท Remote (Hessen, Germany)
Mar 2019 โ€” May 2021
  • 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.
Project: ListInfoAbout Diagonal Software

Skills

Tools & technologies

The stack I reach for across the ML and engineering lifecycle.

Languages & Frameworks

PythonTypeScriptJavaScriptGoSQLDjangoDjango REST FrameworkFastAPIFlaskNode.jsReactNext.jsGraphQL

AI/ML & GenAI

PyTorchTensorFlowscikit-learnHugging Face TransformersLLM Fine-Tuning (LoRA, DoRA, Unsloth)RAGLangChainLangGraphPydanticAIModel Context Protocol (MCP)OpenCVLSTMCNNs

Vector & Graph Databases

WeaviateChromaDBpgvectorNeo4jKnowledge GraphsSemantic SearchEmbeddings

Cloud & Infrastructure

AWS (EC2, S3, RDS, Lambda)SageMakerECR / ECSALB / ELBGlueRedshiftStep FunctionsCloudWatchGCP BigQueryDockerKubernetesKEDA

MLOps & DevOps

TerraformAnsibleJenkinsGitHub ActionsArgoCDCI/CD PipelinesBlue-Green DeploymentsApache AirflowApache Kafka

Data & Observability

PostgreSQLRedisPgBouncerPySparkPandasNumPyPrometheusGrafanaLokiOpenSearch

Projects

Things I've built

Selected projects, side experiments and open-source work.

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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.

ReactTypeScriptGoFastAPILangGraphPydanticAIMCPOpenSearch
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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.

FastAPIChainlitLangGraphPydanticAIpgvectorNeo4jDocker
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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.

PythonTensorFlowKerasLSTM
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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.

LLM Fine-TuningLoRAUnslothGemma
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Research

Publications

70+ citations

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.

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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.

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