Vetted Idempotency Professionals

“Full-stack/backend-leaning engineer with experience spanning fintech platforms, internal AI/RAG assistants, real-time analytics systems, and a zero-to-one academic web platform. Stands out for combining hands-on backend and infrastructure work with product ownership, team guidance, and measurable impact like cutting troubleshooting lookup time from 30 minutes to under 8 minutes and creating reusable UI components adopted across multiple projects.”

“Full-stack/product-minded engineer with strong distributed systems depth, spanning Spring Boot/Kafka microservices, Kubernetes observability, and large-scale React/TypeScript frontends. Particularly compelling for teams building real-time operational products: they describe owning payment/inventory services, designing telemetry dashboards for 150+ services, and helping move claims tracking from polling to event-driven architecture.”

“Backend/platform engineer from Dune Security with strong experience turning messy, fragmented workflows into reusable production systems. They’ve built a shared database abstraction layer, integrated multiple enterprise security platforms into a unified workflow, and shipped AWS Bedrock-powered security insight features with guardrails and human review.”

“Backend/AI engineer who has shipped an LLM-powered enterprise support-ticket agent at Comcast, building a production-grade microservices pipeline (FastAPI, SQS, Redis) with strong observability (OpenTelemetry/Splunk/Prometheus/Grafana) and reliability patterns (async, caching, circuit breakers, idempotency). Demonstrated quantified impact at scale—processing 10k+ tickets/day while improving response SLAs and routing accuracy through evaluation and human feedback loops.”

“Backend engineer with TaskRabbit experience building and operating payment/booking services in Python/Django on AWS (ECS + Lambda) with Kafka/SQS eventing. Demonstrates strong production reliability and incident ownership in high-stakes payment flows (idempotency, strict timeouts, retries, monitoring/alerting) plus data/ETL work in AWS Glue and measurable SQL performance wins.”

“Full-stack engineer who built an internal user-activity tracking and reporting system end-to-end using React/TypeScript, Node/Express, and Postgres, deployed on AWS (EC2/ALB, S3/CloudFront) with CloudWatch observability. Emphasizes reliability and data correctness via idempotent ingestion, retries with exponential backoff, backfills/reconciliation, and performance tuning as data scales, and has experience shipping quickly in ambiguous early-stage startup conditions.”

“Backend/data engineer with production experience building secure Django/DRF APIs (JWT RS256 + rotating refresh tokens), background processing with Celery, and strong reliability practices (timeouts, retries/backoff, structured logging, audit trails). Has delivered AWS solutions spanning Lambda + ECS with IaC/CI-CD and built Glue/PySpark ETL pipelines with schema evolution and data-quality quarantine patterns; also modernized a legacy SAS pipeline to Python/PySpark with parallel-run parity validation and phased rollout.”

“Full-stack engineer who built a policy management and notifications platform end-to-end: Java/Spring Boot microservices with PostgreSQL plus a React/Redux UI, deployed on AWS with Docker and Jenkins CI/CD. Demonstrates strong real-world scaling and reliability practices (Redis caching, Kafka, query/index tuning, ACID transactions, locking, and idempotent processing) to handle high-volume concurrent workloads.”

“Backend/distributed-systems engineer (Amazon; also Bank of America) pivoting into robotics software. Built and owned an end-to-end cross-region event processing service for Aurora Global Databases, emphasizing correctness under latency/clock skew, fault tolerance, and strong observability; brings deep Docker/Kubernetes and CI/CD experience to robotics infrastructure and reliability work while ramping up on ROS 2.”

“Built and shipped production systems spanning real-time operational dashboards and an LLM-powered internal documentation assistant using RAG (embeddings + vector DB). Demonstrates strong focus on reliability and iteration: implemented guardrails and evaluation loops (human review, hallucination tracking, regression prevention) and improved performance/scalability through query optimization, caching, and retrieval tuning.”

“Data engineer with experience at Wells Fargo and Accenture owning end-to-end production pipelines processing hundreds of millions of transactional/risk records daily. Strong focus on data quality and reliability (reconciliation checks, schema drift detection, CloudWatch alerting) plus Spark performance tuning and idempotent backfills using Delta Lake/merge logic across AWS (S3/EMR/Databricks/Redshift) and Azure (ADF/Azure DevOps/Azure Monitor).”

Manasa Reddy Nagendla

Screened

Mid-level Full-Stack Java Engineer specializing in microservices, cloud, and event-driven systems

Cincinnati, OH6y exp

“Software engineer at Procter & Gamble focused on warehouse/operations systems, building near-real-time order/inventory visibility using Java/Spring Boot, React, Kafka, PostgreSQL, and Redis with measurable latency and load-time gains. Also shipped internal LLM/RAG knowledge assistants grounded in company runbooks and workflows, implementing guardrails and an evaluation loop that drove concrete retrieval improvements (document chunking) and regression prevention.”

Ramtin Kazemi

Screened

Junior Full-Stack Software Engineer specializing in Django, AWS, and AI/ML

San Diego, California1y exp

“Full-stack engineer who built and owned an AI-powered personal statement editor in Next.js (App Router + TypeScript), including dynamic routing, server-side data fetching, and typed API route handlers. Post-launch, they handled production monitoring/debugging and shipped reliability/performance upgrades (rate limiting, retries, rollback, DB indexing), and report a 40% latency reduction using Suspense/streaming and React concurrency patterns. Also implemented a durable Temporal-orchestrated AI document workflow with robust retry/idempotency strategies.”

Jake Lee

Screened

Junior Full-Stack/Systems Engineer specializing in AI, embedded systems, and healthcare apps

Boston, MA3y exp

“Led architecture for “Solstice/Solstis,” a safety-aware, hands-free AI medical assistant that guides users through minor emergencies with a structured, state-machine-driven LLM agent integrated with device hardware. Built RAG grounded in Red Cross procedures plus guardrails, fallbacks, and emergency escalation, and improved real-world usability by shifting from open-ended chat to a deterministic step-by-step workflow measured via completion rate, repeat prompts, and latency.”

Ashwitha Reddy

Screened

Mid-level Full-Stack Software Engineer specializing in Java/Spring microservices and AWS

Ohio, United States3y exp

“Backend/platform engineer who has owned a real-time business analytics dashboard backend (Python/Flask/MongoDB) and built Kafka event-streaming pipelines with idempotent processing and DLQs. Strong DevOps/GitOps experience deploying containerized microservices to AWS EKS with CI/CD (Jenkins/GitHub Actions/CodePipeline) and ArgoCD auto-sync/drift detection, plus hands-on support for phased hybrid cloud/on-prem migrations using feature flags and replication.”

Kavya Aitagoni

Screened

Junior Full-Stack Engineer specializing in web platforms and live events

San Francisco, CA2y exp

“Full-stack product engineer with experience shipping user-facing web products end-to-end, including an event analytics dashboard and checkout improvements at Eventbrite. Stands out for combining frontend polish, backend reliability, and production-minded practices like idempotent APIs, query optimization, CI/CD, logging, and monitoring to improve conversion and reduce engineering dependency.”

“Built and deployed an AI-driven expense categorization workflow integrating OpenAI API and PGVector to automate general ledger coding. Stands out for combining LLM/embedding architecture with finance operations context, stakeholder-facing deployment ownership, and measurable impact of roughly 30%+ reduction in manual coding effort.”

“Built and shipped a production Python service integrating with a helium mass spectrometer for real-time sensing and remote control/monitoring. Combines hardware integration, observability, and Playwright-based automation expertise, with a strong track record of turning brittle or ambiguous real-world processes into reliable systems— including reducing dashboard automation failures from about 20% to under 2%.”

“Engineer working on high-throughput financial systems who uses AI pragmatically to accelerate development without sacrificing design ownership, correctness, or compliance. Particularly interesting for teams building regulated, real-time platforms: they have hands-on experience integrating fraud detection models into microservices, handling transaction ingestion, scoring, decisioning, and throughput-sensitive asynchronous workflows.”

“Backend engineer with experience evolving a high-volume agricultural loan processing platform (APMS) at HDFC Bank, emphasizing transactional integrity, auditability, and modularity while integrating with credit bureaus, document management, and risk engines. Also improved automation/reporting robustness at Trend Micro by catching duplicate-event retry edge cases and adding idempotency safeguards.”

“At KPMG, built and productionized a secure RAG-based LLM assistant that lets business and risk stakeholders query data warehouses in natural language, reducing dependence on data engineers for ad-hoc analysis. Demonstrates strong production rigor (Airflow orchestration, CI/CD, containerization), retrieval/embedding tuning (rechunking, semantic abstraction for structured data), and reliability controls (confidence thresholds, refusal behavior, monitoring and canary evals).”

“Full-stack engineer with experience at Vanguard, PNC, and Humana building customer-facing investment/banking flows and internal ops tools using Angular/React/TypeScript with Spring Boot microservices. Strong in shipping time-sensitive changes safely via automated testing/CI (JUnit/Mockito, Jenkins, SonarQube) and in operating event-driven microservices with Kafka (idempotency, retries, correlation IDs). Improved internal tool adoption by responding to ops/support feedback with query optimization and clearer search results.”

“ML/GenAI engineer with production experience building an LLM-powered governance layer that summarizes verified drift/performance signals into validation reports and release notes, designed for regulated environments with de-identification and non-blocking fallbacks. Strong Airflow-based orchestration background across healthcare and finance, integrating Databricks/Spark and MLflow for scalable retraining/monitoring. Demonstrated ability to partner with non-technical healthcare operations teams to deliver actionable risk-scoring outputs via dashboards and automated reporting.”

“Software engineer with hands-on AWS operations experience who owned an end-to-end manufacturing image ingestion pipeline (on-prem to AWS S3) integrated with MES/WMS. In an early-stage SaaS internship, diagnosed a load bottleneck using K6/New Relic and shipped an NGINX least-connection load-balancing solution that scaled to ~4000 RPS while reducing latency. Also improved maintainability and performance in a React/Node e-commerce codebase, cutting page load time from ~10s to 2.8s.”