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Software engineer with 4+ years of experience building cloud-native, distributed backend systems on AWS and GCP, including serverless event-driven architectures and large-scale data pipelines. Delivered measurable impact such as 75% infrastructure cost reduction, 99.99% availability, and up to 10x concurrency scaling. Hands-on with AI/ML infrastructure and production ML/LLM inference platforms, integrating Anthropic Claude API and speech-to-text solutions. Strong in Infrastructure as Code (Terraform), containerization (Docker/ECS), CI/CD automation, and end-to-end observability for high-throughput systems.…Software engineer with 4+ years of experience building cloud-native, distributed backend systems on AWS and GCP, including serverless event-driven architectures and large-scale data pipelines. Delivered measurable impact such as 75% infrastructure cost reduction, 99.99% availability, and up to 10x concurrency scaling. Hands-on with AI/ML infrastructure and production ML/LLM inference platforms, integrating Anthropic Claude API and speech-to-text solutions. Strong in Infrastructure as Code (Terraform), containerization (Docker/ECS), CI/CD automation, and end-to-end observability for high-throughput systems.

JAYKUMAR PAREKH

Web Developer, Back-End Developer, Programmer, +2

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Software engineer with 4+ years of experience building cloud-native, distributed backend systems on AWS and GCP, including serverless event-driven architectures and large-scale data pipelines. Delivered measurable impact such as 75% infrastructure cost reduction, 99.99% availability, and up to 10x concurrency scaling. Hands-on with AI/ML infrastructure and production ML/LLM inference platforms, integrating Anthropic Claude API and speech-to-text solutions. Strong in Infrastructure as Code (Terraform), containerization (Docker/ECS), CI/CD automation, and end-to-end observability for high-throughput systems.…Software engineer with 4+ years of experience building cloud-native, distributed backend systems on AWS and GCP, including serverless event-driven architectures and large-scale data pipelines. Delivered measurable impact such as 75% infrastructure cost reduction, 99.99% availability, and up to 10x concurrency scaling. Hands-on with AI/ML infrastructure and production ML/LLM inference platforms, integrating Anthropic Claude API and speech-to-text solutions. Strong in Infrastructure as Code (Terraform), containerization (Docker/ECS), CI/CD automation, and end-to-end observability for high-throughput systems.

Available to hire

Software engineer with 4+ years of experience building cloud-native, distributed backend systems on AWS and GCP, including serverless event-driven architectures and large-scale data pipelines. Delivered measurable impact such as 75% infrastructure cost reduction, 99.99% availability, and up to 10x concurrency scaling.

Hands-on with AI/ML infrastructure and production ML/LLM inference platforms, integrating Anthropic Claude API and speech-to-text solutions. Strong in Infrastructure as Code (Terraform), containerization (Docker/ECS), CI/CD automation, and end-to-end observability for high-throughput systems.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Language

Javanese

Advanced

Aragonese

Beginner

Work Experience

Software Engineer (Research Aide) - ML Inference Platform at Arizona State University

January 1, 2025 - May 1, 2026

Designed and deployed a production ML inference platform serving 100–2,000+ concurrent users on an event-driven serverless architecture using AWS Lambda, SQS, and ECS. Achieved 99.99% uptime with sub-100ms request acknowledgment. Reduced infrastructure costs by 75% by migrating from persistent GPU-enabled EC2 to elastic pay-per-use serverless components, enabling 10x concurrency scaling during high-traffic research demonstrations. Decoupled compute-heavy YOLO inference from synchronous API flows using SQS queues and containerized ECS workers, eliminating request timeouts and enabling horizontal GPU scaling. Implemented CloudWatch metrics, alarms, and structured logging for end-to-end observability including queue depth, inference latency, and worker health.

Software Engineer at Infocusp Innovations (client project for Google)

June 1, 2022 - July 31, 2024

Architected a fault-tolerant, auto-scaling distributed data processing system on GCP using Dataflow (Apache Beam) ingesting data from 50+ geographically dispersed seismic sensors. Improved downstream ML training throughput by 40% by replacing manual VM-based workflows. Built a Python signal compression module using Nyquist-compliant downsampling and FFT-based frequency analysis; stored compressed NPZ signals to Cloud Storage while preserving reconstruction fidelity for ML pipelines. Designed a high-performance Google Spanner schema for indexing petabyte-scale sensor metadata to reduce metadata query latency by 60%. Used Terraform for version-controlled infrastructure provisioning and authored pytest suites to ensure correctness and reliability of pipeline utilities.

Software Engineer Intern at Crest Data Systems

January 1, 2022 - June 30, 2022

Developed a RESTful API backend using Node.js and PostgreSQL for HR data supporting 1,000+ employees. Optimized relational schemas and indexing to provide the recruiting team real-time visibility into resource availability. Created an automated CI/CD pipeline using Docker and GitHub Actions to reduce deployment overhead by 60% by eliminating manual AWS EC2 deployment steps and standardizing staging and production environments.

Software Engineer Intern at Goodera India (Remote)

June 1, 2021 - December 31, 2021

Automated a global non-profit discovery pipeline using Selenium and Scrapy to replace manual research and save 100+ hours per month. Built a centralized Angular dashboard to visualize and filter non-profit data, integrating Google Apps Script to automate outreach and accelerate stakeholder onboarding.