A Firebase-hosted web experience built as a modern front door for connection and engagement. It focuses on clear “connect with us” flows, approachable UI, and a calm, sensory-forward feel—ideal for brands and communities that want presence and interaction without clutter.
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Naveen Gudimilla · Cloud Solutions Analyst
Cloud Solutions Analyst · Multicloud · AI-augmented platforms
Multicloud platforms, AI workflows, and shipping with confidence.
Around four years designing and operating multicloud, AI-augmented systems: OpenAI-powered pipelines that improved efficiency by 30–35%, containerized apps with GitHub Actions cutting release cycles by ~40%, and Terraform + Kubernetes on AWS and Azure with DevSecOps baked in. MS Information Technology (Marist). MBA Finance (in progress).
Building now
Active products and platforms — design, backend, and AI layers evolving in parallel.
3rd Eye Feel
Connection-first web app on Firebase—sensory, intuitive UX for audiences who value clarity and engagement.
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Firebase
UX
Engagement
GNK Continuum
Intelligent task automation—continuous workflows, less manual busywork, more focus on outcomes.
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Automation
Workflows
Platform
DevOps Dashboard → AI social productivity
Full-stack React + TypeScript + Vite with Firebase (Auth, Firestore, Functions): tasks, collaboration, public routing, and an embedded AI assistant — evolving into an AI-native productivity and social layer.
Code & updates on GitHub →
React
Firebase
AI assistant
LLMs, servers & AI proxy
How I keep AI useful, observable, and safe in real deployments.
I’ve implemented Python backends (Flask, FastAPI) that orchestrate LangChain pipelines, call OpenAI-compatible APIs, and normalize responses for clients. A dedicated proxy layer sits between apps and model providers so keys stay server-side, traffic can be rate-limited, prompts can be audited, and you can swap models without rewriting the UI.
Patterns I use: structured outputs, retrieval-aware prompts, resilient retries, and CI/CD so model-backed services ship like any other service — containers, tests, and GitHub Actions.
OpenAI API
LangChain
FastAPI / Flask
Docker
GitHub Actions
Cloud services
Depth across AWS, Firebase, and GCP-oriented building blocks — full map →
AWS
- EC2, Lambda, S3, RDS, IAM, VPC
- CloudWatch, SNS, Glue, Athena, QuickSight
- Serverless data paths & boto3 automation
Firebase & app backend
- Authentication, Firestore, Cloud Functions
- Security rules & modular React clients
GCP (core skills)
- Cloud Run / Functions-style workloads
- IAM, Cloud Storage patterns, observability hooks
- Integration with Firebase & multi-cloud setups
AI applications I use daily
Tooling that speeds design, code, and research — paired with disciplined review before production.
Cursor
ChatGPT
Claude (Anthropic)
GitHub Copilot
Google Gemini
Perplexity
Midjourney / design assist
LangChain ecosystem
See rationale and links on the Insights page.
Selected engineering work
Shipped pipelines, agents, and infrastructure — full project list →
QueryAgent — AI SQL assistant
LangChain + OpenAI + Python: natural language to SQL for analysts without sacrificing governance.
Repository →Netflix analytics — AWS data pipeline
S3, Glue, Athena, QuickSight — cataloged data, SQL at scale, dashboards for stakeholders.
GitHub profile →AWS infrastructure lab
VPC, IAM, EC2, S3, CloudWatch — boto3 automation and security-first defaults.
Repos →Alexa skill — HVCS
Lambda + Python voice workflows for concierge services — real AWS production patterns.
Repository →DegreeWorks UX prototype
Figma-forward academic planning — GPA, credits, responsive flows.
Repository →More on GitHub
NLP utilities, forecasting experiments, and automation experiments — always iterating.
All repositories →Insights & writing
Long-form posts and notes — often cross-posted from LinkedIn.