Vetted Java EE Professionals

“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

Screened

Mid-Level Software Engineer specializing in cloud-native microservices and event-driven systems

San Francisco, CA4y exp

“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.”

“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

Screened

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

Seattle, WA6y exp

“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.”

Vasudha Prerepa

Screened

Mid-Level Java Full-Stack Developer specializing in cloud-native microservices

5y exp

“QA/validation-focused engineer with experience at Meta testing an ML+LLM content classification/summarization system, including production-vs-test behavior gaps. Built automated E2E validation and drift monitoring (PSI, KL divergence, embedding cosine similarity) run daily/multiple times per day and gated via CI. Also implemented Jenkins-orchestrated Selenium/API test suites in Docker at Capgemini and partnered with a business analyst to convert business rules into automated AI-driven validation checks.”

Kiran Kumar

Screened

Mid-level Software Engineer specializing in Java microservices and GenAI automation

USA4y exp

“Software engineer (4+ years) with hands-on production GenAI experience: built an AI incident triage assistant that summarizes production logs for on-call engineers and iterated it using real incident metrics (time-to-signal, triage duration). Also shipped a RAG-based customer support knowledge assistant using embeddings + vector retrieval with strong guardrails (relevance thresholds/abstain, sanitization, auditing) and a formal eval loop (500-query gold set) that drove measurable retrieval improvements.”

Lavanya Chilakalapudi

Screened

Mid-level Full-Stack Developer specializing in cloud-native web apps and APIs

Tampa, FL5y exp

“Backend engineer with experience building microservice-based systems that integrate LLM workflows (code review suggestions, documentation generation, test scaffolding) using REST APIs, Celery/Redis, and OpenTelemetry for observability. Demonstrates hands-on database and performance optimization in PostgreSQL/SQLAlchemy (bulk inserts, lock mitigation, cursor-based pagination) plus multi-tenant data isolation via tenant-aware models, middleware scoping, and schema/row-level strategies.”

Velusamy Ramasamy

Screened

Principal Software Architect specializing in cloud platforms, data engineering, and enterprise security

Seattle, WA29y exp

“Engineering leader with experience defining solutions from business requirements through detailed specifications and implementation, emphasizing cost-aware technology selection. Has led architectural changes including adding IBM Cloud alongside AWS for budget reasons and integrating caching/messaging to improve availability and performance, and describes scaling distributed teams via experienced DevOps/QA hires and structured evaluation.”

“GenAI/ML engineering leader (player-coach) who built and deployed an image-to-text production system for topology/resource diagrams, combining YOLO-based issue detection with an LLM to generate support-ready reports at scale. Heavy AWS stack (SageMaker, Step Functions, Lambda, CloudWatch, FastAPI, Kubernetes/Docker) with KPI-driven optimization (MTTR, P50), including ~21 custom labels and reported 30–50% faster issue identification while processing thousands of images in production.”