Hi, I'm
Jayesh Devre
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Passionate software developer crafting scalable solutions with AWS, Azure, and Java.
class Developer {
constructor() {
this.passion = true;
this.stack = ['AWS', 'Java'];
}
}Building reliable, scalable systems across data, AI, and cloud. No shortcuts, always production-ready.
ARIZONA STATE UNIVERSITY
Master of Science in Information Technology
SAVITRIBAI PHULE PUNE UNIVERSITY
Bachelor of Engineering in Computer Engineering
Experience
What I have done so far
Amazon
- Java
- Spring Boot
- AWS
- Lambda
- SNS
- SQS
- Amazon Quicksight
- DynamoDB
- CloudWatch
- Mockito
- Distributed Systems
Wipro
- Azure
- Java
- C#
- DevOps
- Kubernetes
- Microservices
- Kafka
- RabbitMQ
- Redis
- MySQL
- PostgreSQL
- MongoDB
- Neo4j
- Dynatrace
- ELK Stack
Wipro
- Python
- FastAPI
- GCP
- Apigee
- Databricks
- Spark
- ETL
- CI/CD
- Prometheus
- Grafana
- Monitoring
Projects
A snapshot of systems I've shipped across backend, data, cloud, and AI.
Distributed Voting App
DevOps-focused project: multi-service voting app with Docker, Kubernetes, and full CI/CD via GitHub Actions and Azure Pipelines.
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- What it demonstrates: Emphasizes DevOps - containerization, orchestration (Docker Compose, Swarm, K8s), and automated build/deploy pipelines.
- Problem solved: Showcases production-style deployment and CI/CD for polyglot services (Python, .NET 7, Node.js) with Redis and Postgres.
- Impact: Demonstrates end-to-end DevOps: Docker images, K8s specs, path-based CI builds, and multi-platform image pushes (GitHub Actions, Azure Pipelines).
TherapyAI
Privacy‑focused therapy chat application with crisis detection, conversation memory, and browser‑based TTS.
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- What it demonstrates: Demonstrates ability to apply LLMs safely in a sensitive domain with guardrails.
- Problem solved: Gives users a private, always‑available companion while escalating potential crises faster.
- Impact: Handled intense hackathon traffic with zero downtime and won the event by shipping a polished MVP.
Developer Portfolio
High‑performance, animation‑rich portfolio showcasing backend, cloud, and AI work with responsive layout.
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- What it demonstrates: Acts as a living product to showcase my UX, performance, and frontend engineering decisions.
- Problem solved: Helps recruiters and collaborators quickly understand what I build and how I think about systems.
- Impact: Consistently loads fast on mobile and desktop, and makes it trivial to add new case studies over time.
ThreadBoost
Spring Boot microservice comparing blocking vs non-blocking APIs using CompletableFuture and @Async with custom thread pools.
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- What it demonstrates: Demonstrates practical async programming in Spring Boot and measurable performance gains.
- Problem solved: Shows how non-blocking endpoints improve throughput and reduce latency for CRUD and file I/O.
- Impact: Achieved 95.6% faster CRUD workflows and 85% faster file read/write in benchmarks vs blocking approach.
Heart Attack Prediction System
End-to-end ML pipeline for heart attack risk prediction using AWS EMR, SageMaker, Lambda, Athena, and SNS alerts.
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- What it demonstrates: Shows building a full ML pipeline on AWS: data processing, model training, inference, and analytics.
- Problem solved: Processes patient vitals, trains XGBoost on SageMaker, runs real-time Lambda predictions with SNS alerts.
- Impact: Delivers automated high-risk patient alerts and queryable analytics via Athena on processed and prediction data.
Ops Dashboard
Operations dashboard aggregating logs, traces, and metrics into a single pane of glass for backend services.
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- What it demonstrates: Highlights experience building observability tools that engineers actually use during incidents.
- Problem solved: Helps on‑call engineers see logs, metrics, and traces in one place instead of juggling tools.
- Impact: Shortened incident investigation time by centralizing signals and adding clear SLO and release context.
Skills
Technologies and tools I work with - across parallel stacks.
Languages
Frameworks
Frontend & Tools
Cloud & DevOps
Database & Monitoring Tools
Libraries
Certifications
AI in my workflow
AI is a tool. The choice about how it gets deployed is ours.