Vetted IntelliJ IDEA Professionals

“React-focused engineer who built a full-stack analytics/test-metrics dashboard (React frontend + Python backend) and turned common UI pieces (data tables, filter panels, chart wrappers) into a reusable internal component library with docs, examples, and basic tests. Strong on profiling-driven performance optimization (React Profiler, memoization) and on owning ambiguous internal-tool projects end-to-end; now planning to package internal patterns into public open-source components.”

“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.”

ChinmaySanjay Kawle

Screened

Junior Software Engineer specializing in cloud developer tools and backend APIs

Seattle, WA2y exp

“Summer intern on AWS Lambda tooling team who shipped Finch support in AWS SAM CLI, adding OS/runtime detection and robust fallback behavior to preserve Docker compatibility across developer environments. Also built an end-to-end RAG system for querying arXiv quantitative finance papers using Postgres/pgvector with two-stage retrieval, citation-grounded prompting, and rigorous evaluation loops driven by IR metrics and user feedback.”

“Full-stack engineer with experience spanning Amazon IMDb and Northeastern’s NeuroJSON portal, combining consumer product work with complex scientific data applications. Built IMDb’s streaming providers feature—described as the company’s most impactful feature of 2023—and has hands-on experience with React/Angular, GraphQL, AWS, Python services, and production monitoring.”

Sunil Chinthaparthi

Screened

Senior Software Engineer specializing in cloud-native backend and distributed systems

New York, NY4y exp

“Backend engineer focused on Python/FastAPI microservices running on Kubernetes (AWS EKS) with strong GitOps/CI/CD ownership (GitHub Actions + ArgoCD). Demonstrated measurable performance wins (p95 latency cut from >1s to <200ms) and production reliability work across Kafka/Redis streaming and cloud-to-on-prem migrations (RDS/S3 to Postgres/MinIO) using parallel validation and checksum-based consistency checks.”

“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).”

Pratima Singh

Screened

Senior Full-Stack Software Engineer specializing in FinTech, cloud microservices, and blockchain

Tempe, AZ10y exp

“Python/ML engineer with strong DevOps depth: built an end-to-end regime-aware stock prediction system (custom fine-tuned FinBERT sentiment + technical/macro features) delivering a 12% accuracy lift. Also implemented Kubernetes/Helm + Jenkins/GitHub Actions pipelines (including GitOps-style workflows for multi-cloud Hyperledger Besu) and improved deployment speed/stability by ~50% while addressing race conditions and image drift.”

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.”

“Backend engineer who built and evolved an internal multi-agent AI research platform (Electron + FastAPI) integrating OpenAI, focused on fast, reproducible experimentation with strong observability and run metadata for debugging. Has led incremental backend refactors with feature flags and parallel validation, and brings production-grade access control expertise from ServiceNow (table/field ACLs and row-level-style enforcement).”

“Backend-leaning full-stack engineer with e-commerce and analytics experience who modernized synchronous order workflows into a Kafka-based event-driven architecture (Java/Spring Boot) to reduce checkout latency and peak-traffic failures. Has built production FastAPI services with JWT/RBAC and strong testing/observability, delivered React+TypeScript reporting dashboards, and handled AWS scaling incidents end-to-end (RDS read latency mitigated with read replicas and query tuning).”

“Full-stack engineer building real-time internal banking operations dashboards (Java/Spring Boot microservices + React/TypeScript) with Kafka-based streaming and post-launch performance optimizations. Also shipped a production internal AI support assistant using RAG (Confluence/PDF/support docs ingestion, embeddings + vector DB retrieval) with guardrails, evaluation loops, and observability to reduce hallucinations and prevent regressions.”

“End-to-end deployment owner for Oracle document delivery/print services in a hospital-like production environment, focused on reliability/performance at scale (thousands of systems). Also describes implementing event-driven RAG/agentic LLM workflows with attention to embeddings/index consistency, latency, and measurable improvements in response relevance and operational efficiency.”