Vetted Java Persistence API (JPA) Professionals

“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.”

“Data engineer focused on large-scale ETL/ELT pipelines across cloud stacks (GCP and AWS), including Spark-based transformations and orchestration with Airflow. Has experience loading up to ~2TB per BigQuery target table and designing atomic loads to multiple downstream systems (Elasticsearch + Kafka), with Kubernetes deployment and Jenkins CI/CD.”

“Backend engineer with experience at JPMorgan Chase and Walgreens, owning transaction-processing and prescription data flow systems in regulated environments. Brings strong hands-on depth in Spring Boot microservices, Kafka, Redis, Kubernetes, observability, and production incident resolution, plus practical experience integrating OpenAI-powered workflows with validation and fallback safeguards.”

“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.”

Shuju Sun

Screened

Mid-Level Software Engineer specializing in real-time data pipelines and ML deployment

PA, USA4y exp

“Ticketmaster data engineer who built CDC-driven Kafka pipelines feeding Snowflake for analytics and data science teams. Hands-on in production operations—scaled Kafka during sudden playoff-driven transaction spikes and improved monitoring for preemptive scaling. Known for using small-batch experiments and quantitative metrics to align stakeholders and drive cost-saving architecture changes (e.g., buffering to reduce AWS Lambda invocation frequency).”

“Sole engineer on a large-scale authentication migration moving 350+ tenants from password-based auth to OAuth 2.0 client credentials, delivering zero downtime, full adoption, and no support tickets. Also has early hands-on experience with agentic LLM proof-of-concepts and has built structured workflow tooling to reduce ambiguity in customer-to-engineering handoffs.”

“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.”

“Backend/AI engineer who built and scaled an internal AMD semiconductor manufacturing microservice platform (SMR), reworking a synchronous lot-request workflow into an event-driven RabbitMQ/Celery/FastAPI pipeline. Diagnosed and fixed peak-load reliability issues using deep observability and Kubernetes autoscaling, cutting notification latency back to sub-second and reducing duplicates via idempotency/DLQs. Also shipped an LLM-powered natural-language search with schema-constrained JSON outputs and guardrails, plus a plan-execute-verify Jira bug-resolution agent that can propose fixes and raise PRs under restricted permissions.”

Rohith Sadanala

Screened

Mid-level Machine Learning Engineer specializing in Generative AI and MLOps

Missouri, USA3y exp

“LLM/agent engineer who has shipped production RAG chatbots in sustainability-focused domains, including a packaging recommendation assistant that standardized messy user inputs and used Pinecone-backed retrieval over product/regulatory data. Experienced orchestrating end-to-end ML workflows with Airflow and AWS Step Functions/Lambda, emphasizing reliability (property-based testing, circuit breakers, OpenTelemetry) and measurable performance (latency/cost). Partnered closely with non-technical leadership to ship 3 weeks early, driving adoption by 150+ businesses and ~20% reported waste reduction.”

“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.”

“Backend engineer focused on cloud/distributed systems, deploying Java 17/Spring Boot microservices on AWS EKS with RDS and Kafka. Demonstrated strong production readiness work (DB lock mitigation, Kafka idempotency, gradual rollouts) and delivered a major latency improvement (~400ms to ~100ms). Also has proven cross-layer troubleshooting skills, isolating intermittent API timeouts to a specific Kubernetes node’s network interface issue, and partners closely with ops teams to build dashboards and workflow automation (including Python scripts).”

“Worked on payments and wallet transactions, with an emphasis on observability and root-cause analysis. Delivered end-to-end A/B testing optimization and implemented Jenkins-based CI/CD automation that reduced manual implementation to 35% and cut deployments to ~2 minutes, with attention to operational considerations like on-call/call rotations.”