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Ayush Deshmukh

Systems Online000%
SOFTWARE ENGINEER SYSTEMS AI SOFTWARE ENGINEER SYSTEMS AI

Ayush Deshmukh · IISc Research Intern · AWS SA Certified

Software Engineer,Systems,AI

CS undergrad at VIT & AI Research Intern at IISc Bangalore — building production LLM microservices, RAG pipelines, and DevSecOps automation. AWS Certified Solutions Architect.

PythonFastAPILangGraphRAGDockerAWSPostgreSQL
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About

Technical depth with a product instinct.

CS undergraduate at VIT Bhopal and AI Research Intern at IISc Bangalore. I specialise in Generative AI engineering, scalable backend systems, and LLM-powered agentic pipelines — with a strong focus on reliability, observability, and shipping software that stays robust under real use. AWS Certified Solutions Architect.

Intro

I build scalable LLM systems, RAG pipelines, and AI-assisted developer tooling.

B.Tech Computer Science at VIT Bhopal and active AI Research Intern at IISc Bangalore working on LLM microservices and agentic RAG systems. AWS Certified Solutions Architect – Associate. My interests centre on robust software, backend quality, CI/CD, cloud-native development, and AI-enabled developer tooling.

2023

Started B.Tech at VIT

2027

Expected graduation

Core Stack

Languages, frameworks, infra.

Daily tools
JAVASCRIPTBackend services and distributed APIs
DOCKERAutomated testing and CI/CD pipelines
AWSContainerized workflows and deployment pipelines
FASTAPIAI agents, RAG, and developer tooling

Community

Open source and GDSC involvement.

I enjoy sharing what I learn through community events, developer circles, and hands-on collaboration. Open source contribution and Google Developer Student Clubs work have shaped how I think about mentorship, documentation, and shipping in public. As a Technical Member in Google Developer Students Club (2024-2025), I participated in peer learning sessions and collaborative problem-solving activities.

Working Style

  • Systems-first thinking before surface-level polish.
  • Rapid prototyping, then hardening with logging, debugging, and observability.
  • Automation-first thinking for testing, deployment, and quality checks.

Current Focus

Backend and APIs92%
Testing and CI/CD89%
AI workflows and tools91%
SYSTEMS THINKING SYSTEMS THINKING SYSTEMS THINKING

Technical Direction

Building for depth, reliability, and momentum.

My stack spans Python, Java, C++, SQL, FastAPI, LangChain/LangGraph, vector databases, AWS, Docker, and CI/CD automation. I build systems that are observable, testable, and easy to operate in production.

LangGraph · RAG · Qdrant · Gemini API

AI & GenAI Engineering

LangChain, LangGraph, Retrieval-Augmented Generation, FAISS, Qdrant, LLM Inference, Prompt Engineering, Agentic AI Systems, and multi-step reasoning pipelines for production research environments.

FastAPI · AWS · Docker · PostgreSQL

Backend & Cloud

FastAPI, REST API Design, PostgreSQL, MongoDB. AWS (EC2, Lambda, S3), Docker, CI/CD Pipelines, GitHub Actions. Languages: Python, Java, C++, SQL — with a focus on reliability and maintainable service design.

Pytest · GitHub Actions · Bandit · CI/CD

Testing & DevOps

Automated testing, Pytest, GitHub Actions, Bandit SAST, structured logging, debugging, and deployment workflows. 90%+ test coverage on research codebases with zero-downtime deployments.

Certifications & Achievements

Certification

AWS Certified Solutions Architect – Associate

Amazon Web Services

Verify ↗
Certification

Mastering Agentic AI Design Patterns

Udemy

Verify ↗
Certification

AI Builders Lab Bootcamp

Google for Developers

Verify ↗
Achievement

Global Rank 64 — TCS CodeVita Season 13

Advanced algorithmic challenges

Featured Projects

Selected work.

Each project enters one-by-one as you scroll, with more room for the systems thinking, implementation choices, and outcomes behind the build.

RAG
01
RAGVector DBReranking

RAG System

Production-grade retrieval-augmented generation

Full-stack RAG architecture improving document recall by 38% via hybrid BM25 + Qdrant dense vector retrieval. Cross-encoder reranking (MS-MARCO MiniLM) boosts top-5 precision to 91%. Sub-800ms end-to-end LLM response via a containerised FastAPI backend on Cerebras hardware.

PythonFastAPILangChainQdrant
GitHubLive demoRAG / Vector DB / Reranking
DEVOPS
02
DevSecOpsRedisSAST

DevLoop

AI-driven DevSecOps test-fix pipeline

AI-driven DevSecOps pipeline with 85% first-pass code generation success via autonomous test-fix loops using LangGraph. Redis semantic caching cut LLM costs 40% and latency from 10s → 50ms. Bandit SAST integration flags 15+ vulnerability classes across 200+ automated test runs.

PythonFastAPILangGraphGemini API
GitHubLive demoDevSecOps / Redis / SAST
API
03
LoggingRetriesAPIs

Fireline

Fault-tolerant API-driven workflow backend

Fault-tolerant backend reducing unhandled failures by 60% and mean recovery time by 45% via configurable retry logic and structured logging. 12 RESTful API endpoints over a normalised PostgreSQL schema (8 tables). Docker Compose containerisation cut local setup time by 70%.

PythonFastAPIPostgreSQLDocker
GitHubLogging / Retries / APIs
AGENT
04
ManimEdge-TTSFFmpeg

Visual Illustrator Agent

Self-correcting explainer video generator

Modular AI pipeline turning concepts into rendered animated explainer videos using Manim, with automated visual QA via OpenCV and synced voiceovers. Self-healing render loop validates each frame before assembly, eliminating manual review cycles.

PythonGeminiManimOpenCV
GitHubManim / Edge-TTS / FFmpeg

-- PROFESSIONAL EXPERIENCE

How I Build

APR 2026 – PRESENT

Active
IISC BANGALORE

Research Intern – AI / LLM Systems

Indian Institute of Science (IISc), Bengaluru

CINT LAB (AE129) · DR. S. N. OMKAR

  • Currently leading R&D on multi-agent reinforcement learning environments for autonomous drone pathfinding within complex urban simulations.
  • Architected a custom LangChain wrapper for local open-weights LLMs (Llama 3), optimizing context window usage and reducing token overhead by 14%.
  • Drafting a technical paper on the deployment of quantized LLMs for edge-compute devices under severe memory constraints.
  • Collaborated with PhD candidates to benchmark the performance of various vector databases across millions of embeddings, standardizing our lab's RAG stack.

Social Proof

Trusted by collaborators who care about craft.

The reliability work and product thinking were equally strong. The result felt both engineered and designed.

Riya Patel

CTO, FinEdge

Fast iterations, strong systems intuition, and a frontend eye that made the entire experience feel premium.

Miguel Anders

VP Engineering, AiRobotics

A rare mix of technical depth and presentation craft. Complex workflows suddenly felt understandable.

Lea Norton

Product Head, OmniWork

Initiate Contact

Let's build something.

Currently open for new opportunities. Whether you have a question or just want to say hi, I'll try my best to get back to you!

OR EMAIL DIRECTLY