Venkata Siva Rama Sarma Maruvada

Full Stack Engineer

Seattle, Washington, United States4 yrs 7 mos experience
Most Likely To SwitchAI Enabled

Key Highlights

  • Expert in building AI-native systems end-to-end.
  • Reduced latency by 30% in high-performance applications.
  • Innovative developer with a USPTO provisional patent.
Stackforce AI infers this person is a Full-Stack Engineer specializing in AI-driven SaaS solutions.

Contact

LinkedIn

Skills

Core Skills

ReactPythonJavascriptMysql

Other Skills

Cloud ServicesCloud InfrastructureTypeScriptFastAPIMongoDBAWSGoogle CloudSQLHTML5CSS3React HooksREST APIsHealthcareTailwind CSSSupabase

About

I build things end to end — frontend, backend, AI layer, infra. Not because I have to, but because I hate handoff gaps where things break and nobody owns it. Most of my recent work has been in AI-native systems. At Augmented AI, I shipped a RAG pipeline that let law enforcement query collision events in natural language instead of digging through databases. Before that, Cognizant supporting Google Maps Street View — processing ~1,000 geospatial images per person per day through distributed cloud pipelines. The scale was real and the margin for error was basically zero. On the side, I'm building Quillio — a Career Passport platform where you fill out your profile once and AI tailors it to any job description in real time. It's a full product: React + TypeScript frontend, FastAPI backend, RAG pipeline, MCP-based orchestration layer, deployed on Vercel + AWS. It started as a weekend idea and turned into a USPTO provisional patent filing. Stack I work in daily: React, Next.js, TypeScript, Python (FastAPI/Flask), PostgreSQL, MongoDB, AWS, GCP, Docker. If you're building something in AI infrastructure, developer tools, or growth-heavy products — I'm probably worth talking to.

Experience

4 yrs 7 mos
Total Experience
1 yr 6 mos
Average Tenure
1 yr 11 mos
Current Experience

Augmented ai

Full Stack Engineer

Jun 2024 – Present · 1 yr 11 mos · Remote

  • At Augmented AI, I contributed to the architecture and evolution of a real-time, AI-powered traffic incident intelligence platform supporting law enforcement and legal analytics.
  • Built the frontend application from scratch using React (TypeScript, Vite), delivering performant dashboards to visualize collision events, vehicle trajectories, and intersection-level analytics.
  • Developed backend services in Python (FastAPI) to process and expose structured metadata generated from NVIDIA VSS-integrated video inference pipelines.
  • Designed low-latency clip streaming and retrieval workflows over NVIDIA VSS, improving end-to-end incident visualization performance.
  • Co-designed and normalized a MongoDB schema to structure AI detections into searchable, time-indexed metadata (timestamps, coordinates, vehicle IDs, lane position, impact vectors).
  • Implemented a Retrieval-Augmented Generation (RAG) layer to enable conversational querying and contextual summarization of traffic events.
  • Led recurring technical syncs with NVIDIA’s VSS engineering team to validate integrations, benchmark performance, and improve streaming throughput.
  • Managed cloud deployments across AWS (S3, EC2) and supported migration to Google Cloud VM instances, containerizing services with Docker and automating CI/CD pipelines.
  • Reduced end-to-end latency by 30% through performance optimization across frontend rendering and backend data pipelines.
Cloud ServicesCloud InfrastructureReactTypeScriptPythonFastAPI+3

Cognizant

Software Engineer

Mar 2020 – Dec 2021 · 1 yr 9 mos · On-site

  • Contributed to a large-scale, data-intensive Google Maps initiative focused on improving geospatial data accuracy, processing efficiency, and distributed system reliability.
  • Key Contributions:
  • Developed Python-based data processing and validation pipelines to handle high-volume structured and unstructured geospatial datasets.
  • Built and maintained internal frontend tools using JavaScript to support operational workflows and real-time data visualization.
  • Automated QA and validation workflows, reducing manual effort by 30% and improving data accuracy by 15%.
  • Designed backend integrations and RESTful services to coordinate data ingestion, transformation, and synchronization across distributed systems.
  • Performed SQL-based data analysis and root-cause investigations to resolve inconsistencies across large production datasets.
  • Integrated AWS S3 and Google Cloud Storage for scalable, secure imagery hosting and retrieval.
  • Collaborated with cross-functional global teams to streamline distributed operations, reducing project turnaround time by 20%.
PythonJavaScriptAWSGoogle CloudSQL

Mvp hospital

Software Engineer

Jan 2019 – Dec 2019 · 11 mos · On-site

  • Developed and maintained mission-critical hospital management systems, contributing across both frontend interfaces and backend integrations to streamline patient data workflows and improve operational efficiency.
  • Key Contributions:
  • Designed and implemented responsive dashboards using HTML5, CSS3, JavaScript, and Bootstrap, improving patient data retrieval performance by 40%.
  • Built and integrated RESTful backend APIs to enable real-time updates across admissions, diagnostics, and discharge modules.
  • Optimized MySQL schemas, indexing strategies, and high-volume queries, reducing peak load latency by 30% and improving system scalability.
  • Collaborated closely with clinical and administrative teams to translate healthcare workflows into secure, compliant software solutions.
  • Conducted end-to-end testing, debugging, and production deployments with minimal downtime in a high-availability healthcare environment.
  • Ensured secure data access through structured backend integrations and role-based permission handling.
HTML5CSS3JavaScriptMySQL

Education

The University of Texas at Arlington

Master's degree — Computer Engineering

Jan 2022 – May 2024

GITAM Deemed University

Bachelor of Technology - BTech

Jan 2015 – Jan 2019

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