Shreyas
Revankar

I build systems that make work disappear. From LLM screening pipelines that slash 70% of manual effort, to production platforms serving 500+ users — my work is defined by outcomes you can measure.

70%
Manual effort cut
25%
API latency reduced
30s
Per-candidate screen
500+
Users shipped to
scroll

Engineering systems that think faster than humans can act

My edge is simple: I obsess over the gap between what software does and what it should eliminate. When I built an AI screening pipeline at ASU, I didn't stop at "it works" — I drove it to under 30 seconds per candidate with structured JSON outputs a recruiter can act on immediately.

My stack spans Python, Java, Go, React, and SvelteKit, but what drives me is the layer beneath — LLM prompt engineering, retrieval-augmented generation, and workflow orchestration with tools like n8n and Gemini AI. I care about measurable outcomes, not impressive-sounding architecture.

I'm pursuing my Master's at Arizona State University while actively seeking SWE internships or co-ops in AI integration, backend infrastructure, or full-stack product development.

Education
M.S. Software Engineering
Arizona State University
Aug 2025 – May 2027
Tempe, AZ
Focus: AI Systems & Full-Stack Architecture
B.E. Computer Science (Data Science)
A. P. Shah Institute of Technology
Dec 2021 – May 2025
India
Published peer-reviewed paper at ICRCET 2025
Certifications
AWS Machine LearningData Analytics Virtual InternshipHackerRank Problem Solving (Basic)
Languages
English (Professional)Hindi (Native)Kannada (Native)Marathi (Fluent)

Systems with measurable impact

Every project below has a number attached to it. That's intentional.

01 — Featured

AI Resume Screening Pipeline

From inbox to shortlist in under 30 seconds

Orchestrated an end-to-end AI hiring pipeline using n8n and Gemini AI. The system automatically pulls resumes from Gmail and Google Drive, performs semantic evaluation against job descriptions, and delivers structured JSON outputs — match scores, skill deltas, and recruiter summaries — without a single manual step.

n8nGemini AIPythonGmail APIGoogle Drive APIREST APIsPrompt EngineeringRAG
70%
manual review eliminated
30s
time-to-screen per candidate
100%
consistent output format
02 — Published ResearchPublished @ ICRCET 2025

CropSync — Smart Farming Platform

Predictive AI + blockchain for agricultural intelligence

Full-stack microservices platform combining AI-driven crop forecasting with blockchain-backed data integrity. Integrated real-time weather and market APIs with a multi-layer cache that eliminated 60% of redundant external calls. Containerized with Docker and deployed via CI/CD to AWS, cutting release errors by 35%.

PythonFlaskAWS (S3, RDS, Lambda)DockerGitHub ActionsBlockchainREST APIs
60%
API calls reduced via cache
50%
faster dashboard loads
35%
fewer release errors
03 — Engineering Tool

Agile Scrum Simulation Engine

Modeling real sprint dynamics across 200+ team scenarios

A Java-based backend simulation engine using state-transition logic to model Agile task lifecycles. Built role-differentiated Swing UI for 6 Agile personas with enforced permission scopes. Integrated full CI/CD via GitHub Actions, achieving 40% improvement in simulation execution consistency across all sprint scenarios.

JavaJava SwingJUnitGitHub ActionsMavenGitTaigaOOP
200+
scenarios modeled
40%
CI consistency gained
6
Agile personas supported

Production-grade under real load

Full Stack Developer Intern

Internship
IEEE Bombay Section·Remote
Sep 2023 – Nov 2023
25% API latency reduction on 500+ user platform

Shipped production frontend and backend features for a platform serving 500+ active users, delivering measurable latency improvements and security hardening under real concurrent load.

  • Architected a SvelteKit frontend with a Flask REST backend for a multi-workflow platform, reducing user-facing configuration errors significantly through improved state management and UI clarity.
  • Engineered JWT-based authentication with session invalidation and token expiry policies, eliminating unauthorized access attempts across all production environments with 500+ active sessions.
  • Redesigned backend data models with targeted C++ optimizations, cutting average API latency 25% — from 400ms to 300ms — under high concurrent production load.
  • Designed a role-based access control layer across 4 user tiers, enforcing least-privilege permissions at the API layer and reducing the platform attack surface.
  • Translated product briefs into detailed API contracts and component specs, enabling parallel development across 2 engineers and shipping 3 features ahead of the 10-week deadline.
  • Automated UI regression testing via Tcl scripts, reducing regression test cycle time and increasing release confidence across all delivery iterations.
SvelteKitFlaskJWTRBACC++TclREST APIsPython
Present

M.S. Software Engineering @ Arizona State University — Actively seeking SWE internships 2026

Built for depth, shipped for scale

Hover a category to explore skill depth. Levels reflect real project application.

AI & Automation

Prompt Engineering90%
RAG Systems87%
LLM Integration88%
Gemini AI85%
n8n Automation92%
OpenAI APIs83%

Languages

Python90%
Java85%
JavaScript / TypeScript82%
Go72%
C / C++75%
SQL80%

Frameworks & Tools

React84%
Node.js80%
Flask / Django88%
SvelteKit78%
REST APIs92%
Microservices79%

Cloud & DevOps

AWS (S3, RDS, Lambda)78%
Docker82%
GitHub Actions / CI-CD85%
Linux80%
PostgreSQL / MySQL78%
NoSQL72%
Also familiar with
FigmaAgile / ScrumJiraTaigapgAdminQuery OptimizationGoogle WorkspaceData AnalysisAI/ML API IntegrationWorkflow Automation

Let's build something that matters

I'm actively seeking SWE internships or co-ops for 2026, especially roles involving AI integration, backend infrastructure, or full-stack product development. If your team is building meaningful systems, I'd like to talk.

status.sh
$ get_status
Available for internships (2026)
Open to full-time conversations
Location: Tempe, AZ (open to remote)
Response time: < 24 hrs
$