Vetted Spring Boot Professionals

Pre-screened and vetted.

KS

Staff Product Manager specializing in FinTech and enterprise SaaS platforms

Santa Clara, California9y exp
BILLRochester Institute of Technology
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AS

Senior Software Engineer specializing in cloud, data platforms, and LLM/RAG applications

Fremont, CA7y exp
Volvo GroupSan José State University
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AD

Engineering Leader specializing in cloud-native SaaS and distributed systems

Fremont, CA9y exp
VMwareUSC
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SK

Mid-level Software Engineer specializing in distributed systems and full-stack development

Sunnyvale, CA5y exp
AmazonStony Brook University
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MK

Mid-level Full-Stack Software Engineer specializing in cloud-native and data platforms

Austin, TX5y exp
AmazonUniversity of Texas at Arlington
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AP

Director-level AI and Software Engineering leader specializing in enterprise platforms

San Ramon, CA28y exp
RivianUniversity of Chicago
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RP

Mid-level Full-Stack Software Engineer specializing in AI-powered applications

Seattle, WA5y exp
AmazonUniversity at Buffalo
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CS

Senior Full-Stack Java Developer specializing in cloud-native microservices

7y exp
Goldman Sachs
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SG

Sanskar Gupta

Screened

Mid-level Full-Stack Developer specializing in Java/Spring Boot, React, and cloud microservices

KS, USA4y exp
DeloitteWichita State University

Backend engineer with hands-on experience building Python/Flask microservices using PostgreSQL/SQLAlchemy, JWT auth, Docker, and GitHub Actions CI/CD. Strong in performance and scalability work—migrated heavy processing to Celery/Redis, tuned queries with EXPLAIN ANALYZE and indexing, and delivered 50%+ API latency reduction. Also integrates AI workflows (OpenAI APIs) with batching/caching/fallbacks and has implemented multi-tenant data isolation patterns.

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AA

Arya Arya

Screened

Junior Software Engineer specializing in backend systems and ads platforms

Bengaluru, India2y exp
MeeshoRamaiah Institute of Technology

Candidate has developed a disciplined AI-first engineering workflow that combines design docs, prior PR analysis, testing plans, and multi-agent coordination to accelerate delivery without sacrificing quality. They described acting as a tech lead for AI agents, overseeing code structure, business logic, testing, and service contracts, and reported reducing manual coding effort by nearly 80%.

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LT

Mid-level Software Engineer specializing in full-stack and backend systems

Albuquerque, NM4y exp
Liberty MutualUniversity of New Mexico

Backend-leaning full-stack engineer with experience at Liberty Mutual and Airbnb, building high-scale insurance claims systems (1M+ monthly transactions) and consumer booking/pricing services (120K–180K daily requests). Strong in transactional data integrity, PostgreSQL performance tuning, and production operations (Docker/Jenkins/AWS), with measurable UX/performance wins including ~2.3s page loads and significant runtime failure reduction.

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SF

Sara Fang

Screened

Mid-level Software Engineer specializing in cloud data platforms and distributed systems

Remote6y exp
Terra Byte XUniversity of Delaware

Backend/data engineer with production experience building FastAPI services with strong reliability patterns (circuit breaker, rate limiting, caching, graceful degradation) and JWT/OAuth2 auth. Has delivered AWS EKS deployments via Terraform with Secrets Manager/IRSA and HPA autoscaling, and built Glue/Spark ETL pipelines on S3 Parquet with schema-evolution and idempotent reruns; also demonstrated measurable SQL tuning impact (20–30s to <10s).

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BK

Mid-level Full-Stack Software Engineer specializing in cloud microservices and AI integration

Jersey City, NJ3y exp
UberPace University

Backend/distributed-systems engineer with Uber experience building real-time telemetry and safety signal pipelines. Strong in Kafka-based event-driven architectures, low-latency processing under peak load, and production reliability via monitoring, retries, and fallback logic; has Docker/Kubernetes and CI/CD deployment experience.

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Yeshwanth Sai Pala - Mid-level Full-Stack Developer specializing in cloud microservices and AI-driven FinTech in Remote, USA

Mid-level Full-Stack Developer specializing in cloud microservices and AI-driven FinTech

Remote, USA4y exp
StripeSouthern Arkansas University

Stripe engineer who shipped an end-to-end merchant fraud insights dashboard, spanning Spring Boot/Kafka risk-scoring services and a React+TypeScript UI. Focused on low-latency, high-volume transaction processing and production operations on AWS (EKS/CloudWatch), including handling a real traffic-spike latency incident via query optimization, indexing, and rate limiting.

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SK

Intern Software Engineer specializing in developer productivity and data/AI systems

Los Angeles, California1y exp
IntuitUC Berkeley

Internship experience at Intuit building an LLM-grounded QA system for internal microservice data across 100+ microservices, using a graph database approach (evaluated Neo4j and selected AWS Neptune for production alignment). Also has UC Berkeley research experience (including work with Prof. Dawn Song / Berkeley Eye Research Lab) and cross-functional collaboration with bioinformatics/biology teams to deploy software systems on research servers.

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MO

Mid-Level Software Engineer specializing in cloud-native distributed systems

Bellevue, WA7y exp
AmazonUniversity of Washington

Gameplay engineer with hands-on ownership of a real-time C++ combat ability system, including diagnosing and eliminating large-scale combat frame spikes by refactoring hit detection to an event-driven, animation-notify approach (cut collision checks ~80%). Also implemented UE5 networked abilities (dash) with client-side prediction and server-authoritative reconciliation, plus projectile ballistics validated through debug spline visualizations and unit tests.

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SB

Mid-level Backend & Reliability Engineer specializing in AWS, Kubernetes, and automation

New Mexico, US5y exp
MetaUniversity of North Carolina at Charlotte

Meta engineer focused on reliability/operations tooling who built a unified real-time health dashboard and scalable telemetry pipelines (AWS + Datadog) for thousands of devices. Also shipped an internal LLM-powered knowledge assistant using RAG over wikis/runbooks/logs with strong guardrails and a rigorous eval loop that drove measurable accuracy improvements via automated doc ingestion and embedding updates.

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Shreya Roy Koneri - Mid-level Software Engineer specializing in backend microservices and real-time payments in Phoenix, AZ

Mid-level Software Engineer specializing in backend microservices and real-time payments

Phoenix, AZ5y exp
American ExpressUniversity of Dayton

Product-minded full-stack engineer who has owned customer-facing platforms end-to-end, including a unified web UI platform that increased adoption by 30% using feature flags and phased rollouts. Experienced designing TypeScript/React systems with microservices and RabbitMQ at scale, addressing reliability issues with DLQs, retries, and idempotent consumers, and building internal analytics tooling adopted company-wide within weeks.

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KARTHIKBABU VADLOORI - Mid-level Full-Stack Developer specializing in Spring Boot, React, and cloud microservices in San Francisco, CA

Mid-level Full-Stack Developer specializing in Spring Boot, React, and cloud microservices

San Francisco, CA5y exp
MetaUniversity of Texas at Arlington

Backend engineer with experience at Meta and Accenture building regulated-data systems (healthcare/financial) using Python/Flask and Postgres. Has scaled high-throughput services to millions of daily requests, delivering measurable latency wins (~40% API latency reduction; ~35% faster DB-backed endpoints), and has productionized ML inference services using Docker/Kubernetes and AWS (ECS/SageMaker).

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Zheng Wu - Junior Software Engineer specializing in backend systems and cloud messaging in Mountain View, CA

Zheng Wu

Screened

Junior Software Engineer specializing in backend systems and cloud messaging

Mountain View, CA1y exp
NewsBreakRice University

Data/ML engineer who has owned end-to-end systems across email deliverability/segmentation and production LLM apps. Built a Spark+Airflow segmentation engine that materially improved deliverability (99.9%) and open rates (>50%), and shipped a PDF-to-quiz RAG product using LangChain/Vertex AI/Chroma with strong guardrails and an eval loop that cut hallucinations to <5%.

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