Vetted Java Persistence API (JPA) Professionals

Bhavesh Ittadwar

Screened ReferencesStrong rec.

Senior Full-Stack Engineer specializing in scalable web and cloud systems

USA3y exp

“JavaScript engineer who built a Michelin-specific headless CMS forms platform based on apostrophe-forms, powering forms across 400+ Michelin websites. Designed an extensible, SOLID-aligned modular field architecture with a shared design system, cutting hundreds of lines of per-project code across 10+ implementations while driving cross-device compatibility and performance (BrowserStack, Lighthouse, SSR).”

“Backend/platform engineer in healthcare and consulting (Molina Healthcare, TCS) who productionized real-time eligibility/authorization and care navigation workflows with strong reliability and HIPAA security. Demonstrated measurable performance gains (≈40% latency reduction, ~99% uptime) using Spring Boot APIs, SQS decoupling, Redis caching, and deep observability, and regularly leads technical demos that accelerate client adoption.”

“Full-stack + ML engineer who built “Finsight,” a real-time financial risk platform (React/FastAPI/MongoDB/AWS Lambda) processing 2M+ records monthly, using sharding and Redis caching (60% DB load reduction) plus async and batch optimizations. Also has healthcare product experience at Apollo Healthcare, partnering directly with clinicians/admins to design and iterate EHR dashboards via Figma prototyping and user testing, and demonstrates clear system design thinking for real-time voice-to-LLM architectures.”

“Customer-facing software engineer who rapidly turns business requirements into Figma prototypes and PoC applications, using workflow prioritization and frequent client reviews to stay aligned. Has hands-on experience integrating with existing authentication/user APIs, building MongoDB-backed caching, and implementing robust fallback/retry mechanisms. Comfortable working on-site with customers and resolving production issues in AWS (e.g., DNS/EC2 traffic routing) in collaboration with DevOps.”

“Master’s-program backend engineer with strong Java/Spring Boot industry experience who also owned a Python analytics service (Flask/Postgres, JWT, Celery/Redis) and optimized large-dataset performance via SQL/batching. Has hands-on Kubernetes microservices deployment and GitLab+Terraform CI/CD/GitOps workflows, plus experience supporting phased on-prem to AWS migrations and building Kafka-based real-time streaming pipelines.”

“Built an LLM-powered agent within the Nora AI analytics platform to automate e-commerce product performance analysis and generate actionable recommendations (pricing/inventory), designed with production-grade reliability patterns and observability. Emphasizes predictable, schema-validated tool/function-calling pipelines with robust fallbacks, idempotency, and guardrails for messy operational data.”

“Backend/data engineer focused on fintech payments and fraud systems, owning real-time Kafka-based reconciliation pipelines end-to-end (~13k tx/day). Built audit-friendly validation/reconciliation (SQL + Python), kept lag to seconds, and cut errors ~20%, while also shipping Spring Boot APIs with Redis caching and strong idempotency/versioning. Has early-stage startup experience standing up payment services on AWS with Docker + GitHub Actions and production monitoring/incident handling.”

George Platon

Screened

Principal Full-Stack Engineer specializing in AI, DevOps, and cloud platforms

Romania, Romania16y exp

“Built a production end-to-end AI video-to-reels clip extraction system using a multi-agent architecture with transcription, captioning, effects generation, and centralized orchestration. Demonstrates unusually strong systems thinking around reliability, observability, evaluation, and production tradeoffs for LLM-powered workflows, including Kubernetes/Kafka-based deployment and regression-driven prompt governance.”

“Data engineering/software development candidate who built a stock market pipeline and uses that project to demonstrate strong architectural thinking across Kafka, Spark, and Airflow. They stand out for a pragmatic approach to AI: using tools like Copilot, ChatGPT, LangChain, and AutoGen to accelerate development while maintaining human oversight, testing, and system-level decision making.”