Hi, I'm Amal Chaitanya.

I build software, backend systems, and AI-powered applications.

I'm a software engineer with experience building production applications using Java, Spring Boot, Python, microservices, cloud infrastructure, and modern frontend technologies. I also enjoy experimenting with AI, LLMs, RAG, and developer tools.

0

apps live on Google Play

0K+

downloads and counting

0.0★

highest Play Store rating

0+

user reviews answered

About

About Me

Amal Chaitanya — Software Engineer

I'm a software engineer who enjoys understanding how systems work and turning ideas into working products.

My background is primarily in backend and full-stack development, with experience building applications using Java, Spring Boot, Python, APIs, databases, cloud infrastructure, and modern frontend frameworks.

More recently, I've also been building with LLMs, RAG, AI agents, and voice AI. I enjoy learning through building — whether that's a production application, an AI experiment, a developer tool, or a small product idea.

Career

Experience

Where I've worked and what I focused on.

Software Engineer

JPMorganChase · Contract · Greater Houston · Hybrid

Nov 2022 – Present

CORE application — an internal platform for compliance and operational risk evaluation for banking regulators. Full-stack Java and TypeScript (React), Oracle data, deployments on AWS and PCF Cloud Foundry. Designed Java-based schedulers automating data feeds; explored LLM integration with Hugging Face, LangChain, and Python for compliance checks and data analysis. Scalable APIs with Hibernate and a modular MVC codebase, plus reusable React components.

JavaSpring BootTypeScriptReactPythonMicroservicesREST / GraphQL APIsOracleHibernateAWSPCFKubernetesCI/CDHugging FaceLangChain

Associate Engineer

Thomson Reuters · Hyderabad, India

Jun 2021 – Nov 2021

Backend microservices with Java Spring Boot and Python, MongoDB for financial data storage and processing, and RESTful APIs for front-end communication. React + TypeScript dashboards for financial reporting, transaction processing, and audit trails. Reporting automation with pandas and NumPy; ML models with scikit-learn and TensorFlow for anomaly detection. Jenkins and GitLab CI/CD on AWS.

JavaSpring BootPythonMicroservicesREST APIsMongoDBReactTypeScriptAWSJenkinsscikit-learnTensorFlow

Software Developer Intern

Thomson Reuters · Hyderabad, India

Nov 2020 – Jun 2021

Built and refined applications with Core Java, Python, and modern JavaScript (React + TypeScript) using TDD. RESTful APIs with Spring Boot plus GraphQL; Kafka for real-time streaming; MongoDB and PostgreSQL storage. Docker and Kubernetes deployments with Terraform; JUnit, Jest, and Cypress coverage; Swagger docs; Jenkins and GitLab CI/CD on AWS with CloudWatch and Prometheus monitoring.

JavaSpring BootGraphQLKafkaReactTypeScriptMongoDBPostgreSQLDockerKubernetesAWSJenkins

Software Developer

IFIVEGlobal · Hyderabad, India

Aug 2019 – Jul 2020

Full-stack web applications with React (Webpack, Babel) and Node.js microservices on MySQL, with SOAP integrations. Testing with Jest, Enzyme, and Cypress; AWS, Docker, and Jenkins CI/CD pipelines.

ReactNode.jsMySQLSOAPAWSDockerJenkins

Education

Master's degree, Computer Science

Southeast Missouri State University

Education

BTech, Information Technology

VNR Vignana Jyothi Institute of Engineering and Technology

Stack

Technical Skills

Grouped by area. No percentages — the work speaks for itself.

Backend

JavaSpring BootPythonMicroservicesREST APIsGraphQLHibernate / JPA

AI

LLMsRAGOpenAIGeminiClaudeLangChainEmbeddingsVector SearchPrompt Engineering

Frontend

ReactNext.jsAngularTypeScript

Cloud & DevOps

AWSAzureDockerKubernetesGitHub ActionsJenkins

Databases

PostgreSQLOracleMongoDBRedis

Work

Projects

Selected work — production systems, AI experiments, and shipped mobile apps.

Simple RAG — Ask Your PDF

A simple Retrieval-Augmented Generation application that lets users upload a PDF and ask questions using embeddings, vector search, and an LLM.

simple-rag — ask your pdf
Q: How are chunks ranked?
chunk 14 · p.60.87
chunk 22 · p.90.83
chunk 07 · p.30.79
chunk 31 · p.120.76
Chunks are embedded, FAISS returns top-k by cosine similarity, then the LLM answers [p.6][p.9] only from context.
PythonOpenAIFAISSRAGEmbeddings

Interview Prep Platform

An AI-powered interview preparation platform that identifies knowledge gaps, generates personalized learning plans, and supports AI-powered mock interviews.

● REC · mock — system design08:42
clarity
8.2
depth
7.4
tradeoffs
6.8
JavaPythonLLMsVoice AIReactAPIs

Talking Clock: Time Announcer

Android app that announces the time at intervals you choose — reliable with the screen off, quiet hours, shake-to-hear, fully offline and privacy-first.

4.6★ · ~390 reviews · 10K+ downloads · Google Play

9:41▂▄▆
talking clock · announcer on● every 5 min

10:30 AM

“It’s 10:30 AM” · quiet 11PM–7AM

★ rating
0.0
downloads
0K+
offline
0%
AndroidText-to-SpeechForeground ServiceMaterial You

Loudify: Text to Speech Reader

Free offline text-to-speech reader — PDFs, ebooks, and any text read aloud in 50+ languages. No accounts, no ads, no data collection.

1K+ downloads · Google Play

9:41▂▄▆
loudify · report.pdf▶ 1.25x · EN
“…and revenue grew 18% quarter over quarter…”
12:04 / 48:20PDF · EPUB · DOCX · 0K+
AndroidText-to-SpeechOffline-First

TaskRing: Task Reminder

Private on-device reminder app with reliable alarms — fallback plus nag mode, health checks for aggressive battery savers. No account, no cloud.

100+ downloads · Google Play

9:41▂▄▆
taskring · today✓ health ok
🔔 Call mom · 9:00 AMstreak 6

7-day adherence · 0+ downloads

AndroidAlarmsOffline-First

VidShrink: Video Compressor

On-device hardware-accelerated video compressor — one-tap presets for WhatsApp, Discord, and social. No uploads, no ads, no watermarks.

50+ downloads · Google Play

9:41▂▄▆
vidshrink · hardware h.2640% smaller
248 MB→ 9.6 MB
WhatsApp 720pDiscord <10MBSocial 1080p
AndroidH.264Hardware Encoding

Haircut Coupons USA

A data-driven coupon discovery platform that automatically discovers, validates, and organizes haircut offers from multiple sources.

pipeline · cron 6h✓ 1,284 valid
discover → 312 sourcesdone
normalize + dedupe −48done
validate live pagesrunning
publish static pagesqueued
PythonData PipelinesAutomationSEONext.js

Spotlight · AI

Ask anything.

Simple RAG turns any PDF into answers you can trust — chunked, embedded, retrieved by vector search, and generated with cited sources. No framework magic; every stage visible.
PDF → Chunks → Embeddings → Vector Search → LLM → Answer + Sources
simple-rag — ask your pdf
Q: How are chunks ranked?
chunk 14 · p.60.87
chunk 22 · p.90.83
chunk 07 · p.30.79
chunk 31 · p.120.76
Chunks are embedded, FAISS returns top-k by cosine similarity, then the LLM answers [p.6][p.9] only from context.

Spotlight · Google Play

Hear every minute.

Talking Clock is my most-loved build — 0.0★ from ~390 reviews and 0K+ downloads. Reliable with the screen off, fully offline, no subscription.
9:41▂▄▆
talking clock · announcer on● every 5 min

10:30 AM

“It’s 10:30 AM” · quiet 11PM–7AM

★ rating
0.0
downloads
0K+
offline
0%

Contact

Let's connect.

I'm always interested in discussing software engineering, AI, interesting products, and new ideas.

amal.chaitanya@email.com