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Vetted Java EE Professionals
Pre-screened and vetted.
Senior Full-Stack Engineer specializing in cloud-native web and mobile platforms
Principal Software Engineer specializing in distributed systems and cloud microservices
Meghana kanakam
Mid-Level Java Developer specializing in FinTech microservices
“Backend/platform engineer with deep payments experience who built and operated a real-time transaction routing service end-to-end on AWS (Spring Boot, PostgreSQL/RDS, Redis, Kubernetes), delivering ~40% latency reduction and 99.99% uptime via strong resiliency and observability practices. Also productionized an internal LLM-powered RAG knowledge assistant with guardrails and a user-feedback-driven evaluation loop, and has led incremental monolith-to-microservices modernization using Strangler Fig and shadow traffic.”
Shubham Singh
Mid-level Software Engineer specializing in LLM systems and intelligent search
“Backend engineer from Palantir who built and productionized an enterprise LLM-based document intelligence/search platform, evolving it into a hybrid lexical+vector retrieval system. Emphasizes reliability and cost control via strict LLM gating, robust fallback paths, and evaluation frameworks (e.g., MMLU/BLEU), plus disciplined migration practices (feature flags, dual-writes, shadow reads) to ship changes safely at scale.”
Director-level Technology Leader specializing in IoT, cloud platforms, and wireless networking
Junior Full-Stack Developer specializing in cloud-native microservices
Mid-level Software Engineer specializing in FinTech and compliance systems
Senior DevOps Engineer specializing in cloud security, automation, and data pipelines
Senior Software Engineer specializing in cloud-native microservices and FinTech platforms
Mid-level Software Engineer specializing in FinTech payments and compliance systems
Mid-level Full-Stack Java Developer specializing in microservices and cloud platforms
Mid-level AI Engineer specializing in LLMs, RAG chatbots, and cloud AI testing
Mid-Level Software Engineer specializing in AI, cloud-native microservices, and full-stack systems
Senior Software Engineer specializing in cloud-native microservices and real-time data pipelines
Mahesh Purushothaman
Senior Director of Software Engineering specializing in cloud-native microservices for streaming platforms
“Engineering leader who drove TiVo IPTV’s client-facing API modernization from a monolith to AWS-based microservices (API Gateway, Lambda, EKS, Kafka, DynamoDB/RDS), including phased/blue-green production routing of millions of calls. Emphasizes org scaling through skill-based hiring, mentorship, and a you-build-you-run ownership culture, while balancing technical leadership with executive stakeholder communication and budgeting.”
Anu Baluguri
Mid-Level Software Engineer specializing in cloud-native microservices and event-driven systems
“Full-stack engineer with production experience at Atlassian and Zoho, spanning GraphQL federation, React/TypeScript frontends, and cloud-native AWS/Kubernetes operations. Built and operated a federated GraphQL gateway with Terraform + CI/CD + observability, delivering major latency and integration-time improvements, and also designed high-volume Kafka data pipelines (10M+ events/day) with strong reliability guarantees.”
Jonas Shuai
Mid-level Full-Stack Software Engineer specializing in cloud, microservices, and React/Java
“Software engineer with experience at PayPal and JPMC building large-scale onboarding/account setup systems using React/TypeScript with Spring Boot/Node microservices and Kafka. Also built an Ignition-based SCADA monitoring tool at Mainspring Energy that became the default for manufacturing/test engineers by aggregating real-time telemetry and historical test data.”
Nagarjuna Vaddineni
Mid-level Full-Stack Software Engineer specializing in cloud-native microservices and data pipelines
“Amazon backend engineer who built and operated high-scale Java Spring Boot microservices on AWS (EKS/EC2) handling millions of daily transactions, with deep experience debugging p95 latency and database/ORM bottlenecks. Shipped an AI-driven real-time personalization feature by integrating SageMaker model inference end-to-end with low-latency caching and graceful fallbacks, and designed robust order/payment orchestration with retries, compensations, and DLQ-based escalation.”