Vetted Idempotency Professionals

“Full-stack engineer with strong ownership in fast-moving environments: designed and shipped a pre-order/campaign inventory system (NestJS + Strapi + Datadog) that freed 34% warehouse space and reduced stock risk to ~5.7%. Also built rapid, high-impact logistics features (Spot Sales) that drove last-mile cost to ~0 in ~40 days, and has hands-on AWS/Terraform/CI-CD experience including deploying a global RAG system with Pinecone, Datadog, and PagerDuty.”

“QA automation engineer with deep experience owning end-to-end Cypress/JavaScript test suites (smoke, regression, and API contract tests) integrated into GitHub CI with merge gating and rich reporting. Demonstrated healthcare enrollment domain expertise by catching a critical eligibility versioning/overwrite defect via API + DB assertions that UI tests missed, then hardening the pipeline with contract tests and idempotency checks.”

“Full-stack engineer with healthcare and fintech experience who has owned production features end-to-end—most notably an AI assistant clinical risk summary tool on AWS (FastAPI/Lambda + React/TypeScript) that cut analyst review time ~40%. Strong in performance tuning for large datasets (S3/Athena), production ops/observability (CloudWatch, CI/CD, env separation), and building reliable ETL/integrations with idempotency and retries.”

“Backend/platform engineer with State Farm experience modernizing and scaling an enterprise consolidated payment data platform and event-driven pipelines. Built cloud-native payment architecture (ECS->EKS) handling millions of financial transactions/day and high-volume telemetry (~100M events/day), with strong schema governance (Avro + schema registry) and production operations/incident mitigation driven by observability.”

“Built and shipped a production LLM-powered customer support agent for an EV charging platform using RAG plus internal APIs, automating session/payment issues and ticket routing. Emphasizes production readiness via guardrails, schema validation, state-machine orchestration, monitoring, and continuous evals, delivering a reported 35–40% reduction in support tickets and improved customer satisfaction.”

“Application-focused full-stack engineer in the clinical/health domain who shipped an LLM-powered clinical note summarization workflow end-to-end (FastAPI + Postgres + Kafka workers + React/TypeScript UI) with strong attention to security, auditability, and clinician trust. Has hands-on AWS/EKS operations experience and has resolved real production latency/scaling issues through async processing, query/index tuning, caching, and horizontal scaling.”

Dhairya Desai

Screened

Senior AI/ML Engineer specializing in healthcare NLP and predictive analytics

Chicago, IL13y exp

“ML/NLP engineer with healthcare and industrial IoT experience: built an Optum pipeline that converted 2M+ physician notes into structured entities and linked them with claims/pharmacy data to create an actionable patient timeline. Deep hands-on expertise in production NER, entity resolution, and hybrid search (Elasticsearch + embeddings/FAISS), plus robust data engineering practices (Airflow, Spark, data contracts, auditability) and experimentation-to-production rollout via shadow mode and feature flags.”

Sreedivya Nagalli

Screened

Junior AI/ML Engineer specializing in deep learning and full-stack ML applications

2y exp

“Built and operated a production-used RAG-based AI study planner (GPT-4 + FAISS) that handled 250+ concurrent users, with real-world reliability engineering (caching, fallbacks, schema validation, Redis state, monitoring). Also has healthcare data integration experience at Medinet Analytics, standardizing messy EHR/practice-management data with canonical schemas, idempotency hashing, and compliance-grade audit trails.”

Navya Sureddy

Screened

Mid-level Full-Stack Developer specializing in Angular/React and Spring Boot

Columbus, IN5y exp

“Full-stack engineer with experience at Cummins owning production features end-to-end (React/TypeScript + Node + Postgres) and operating them in AWS (EC2/RDS/S3/IAM) with CloudWatch-based observability. Also built resilient ETL and third-party integrations, including an AWS Glue–S3–Redshift pipeline hardened with validation, idempotent UPSERTs, retries/backfills, and quarantine handling to prevent bad or duplicate data.”

Darshan Togadiya

Screened

Mid-level Full-Stack Engineer specializing in cloud-native web apps

Rancho Cucamonga, CA5y exp

“Full-stack engineer in an early-stage startup who built an EV charger monitoring and payments dashboard from scratch, owning UI/UX (Figma), React frontend, Node/Postgres APIs, and production deployment/ops (Firebase + AWS). Demonstrated measurable impact (40% fewer reconciliation errors) and strong reliability chops through multi-source energy/payment ingestion, idempotent pipelines, and CloudWatch-driven incident resolution.”

“Data engineer focused on large-scale financial data platforms, with hands-on ownership of an AWS + Databricks + Snowflake pipeline processing ~2TB/day. Strong in data quality (Great Expectations), schema drift automation, and production reliability (99.9%), plus measurable performance/cost wins (4h→1.2h, ~25% cost reduction). Also built an async Python crawling/ingestion framework with anti-bot mitigation, retries, and Airflow-driven backfills.”

“Backend/distributed systems engineer with Apple-via-Infosys experience who is applying production-grade engineering patterns to LLM workflows. Built a log summarization and anomaly-surfacing pipeline that cut manual triage by ~30-40%, with strong emphasis on structured outputs, retries, fallbacks, and stability under noisy real-world conditions.”

“Built and shipped a production LLM-powered agent for supply chain operations that integrates ERP data and automates multi-step decision-making with tool calling, state management, and structured JSON outputs. Emphasizes production reliability (guardrails, fallbacks, monitoring, idempotency) and reports strong business impact: 40% faster decisions, 30% higher throughput, and 25% efficiency gains.”

“Founding engineer who built a multi-tenant AWS backend from scratch focused on ultra-fast, configuration-driven client onboarding and low operational cost. Automated tenant provisioning/deployments with Terraform + GitHub Actions (new client infra in ~13 minutes) and scaled to 62 production clients handling ~75k requests/day without a major rewrite. Hands-on with migrations (DynamoDB->MongoDB), reliability/observability, and performance tuning (indexes, Redis, queueing, connection management).”

“Backend-leaning full-stack engineer who has built and operated event-driven microservices platforms (FastAPI/React/TypeScript, Kafka, Kubernetes) and internal DevOps tooling. Delivered measurable impact through user-feedback-driven iteration (WebSockets update mechanism cutting redundant API calls ~30%) and operational improvements (deployment monitoring dashboard reducing rollback time ~40%), with strong focus on reliability, observability, and data consistency at scale.”

“Full-stack engineer who has owned React/TypeScript + Spring Boot dashboard products end-to-end, including real-time performance/alerts and data aggregation across services. Strong in shipping MVPs quickly with feature flags, automated testing and CI/CD, and using monitoring/click-path analytics to prioritize work—achieved a 40% page-load reduction. Experienced operating microservices with RabbitMQ at scale, addressing retries/idempotency/observability and fixing duplicate-processing incidents with idempotent consumer patterns and DLQs.”

“Python backend engineer who owned an end-to-end Django/DRF authentication and account-management module (JWT, RBAC, email verification) and optimized token validation performance. Has hands-on Kubernetes + Helm delivery with GitOps via ArgoCD (multi-environment app-of-apps, drift detection/rollback) and has supported a cloud-to-on-prem migration using staged testing and phased cutover. Also built and scaled a Kafka-based real-time user activity tracking pipeline with reliability and backpressure controls.”

“Backend engineer with airline operations domain experience who modernized flight-ops systems from batch updates to real-time streaming on AWS (Kafka + Spring Boot microservices), improving latency and stability through metric-driven tuning and idempotency. Also shipped a production LLM decision-support component using RAG over operational logs and internal procedures, with strong guardrails and an evaluation/regression loop to reduce hallucinations and enforce grounding.”

Vivekananda Reddy

Screened

Mid-Level Full-Stack Software Engineer specializing in Java, React, and AWS

Dallas, TX4y exp

“Backend engineer focused on cloud-native microservices on AWS, owning Python/Flask ingestion services integrated with S3/Lambda and deployed via Docker/Kubernetes with CI/CD. Has led phased migrations from manually managed EC2 setups to automated CloudFormation + pipeline-driven releases, and designed event-driven near-real-time pipelines with idempotency, retry/backoff, and strong observability.”

“Data engineer with healthcare/clinical trial domain experience who owned a 100TB+/month AWS pipeline end-to-end (Glue/S3/Redshift/Airflow) and drove measurable outcomes (20% lower latency, 99.9% reliability, 40% less manual reporting). Also built production data services and API-based ingestion on GCP (Cloud Run/Functions/BigQuery) with strong validation, versioning, and safe migration practices, and launched an early-stage RAG solution (LangChain + GPT-4) for researchers.”

“Data/analytics engineer focused on finance and e-commerce integrations, building end-to-end pipelines and services across Odoo, QuickBooks, Snowflake, and Tableau. Replaced a costly third-party Walmart connector with a serverless AWS Lambda pipeline deployed via Terraform/GitHub and monitored with CloudWatch/Datadog, and shipped a bi-directional Odoo↔QuickBooks invoice sync with distributed locking plus Slack-based finance approvals.”

“Built and shipped an AI-driven interview evaluation pipeline at SeekOut that automated recruiter screening via a multi-stage LLM agent workflow (.NET backend, RabbitMQ orchestration, Python workers). Emphasizes production-grade reliability (idempotency, retries, strict JSON/schema validation), strong observability with OpenTelemetry, and measurable efficiency gains including ~40% reduction in token usage/cost.”

Xin Li

Screened

Staff Software Engineer specializing in distributed systems and high-concurrency APIs

Santa Clara, CA17y exp

“Full-stack engineer in an insurance + telematics org who built a 0→1 multi-agent, AI-driven mobile regression execution system integrating Jira and n8n, emphasizing determinism (schema-validated outputs), traceability (state-machine style step archives), and scalable device scheduling. Also designed an event-driven microservices architecture using Kafka with standardized API/event contracts and has hands-on incident response experience improving resilience via rate limiting, circuit breaking, caching, and idempotency.”

BhanuPrakash Akepogu

Screened

Mid-level Full-Stack Engineer specializing in FinTech systems

MA, USA4y exp

“Full-stack engineer with strong fintech/financial-systems experience who built an automated reconciliation and reporting system end-to-end (Python/Django/FastAPI + React, Docker on AWS), cutting reconciliation time by ~25% and improving audit traceability. Experienced designing typed REST APIs and Postgres data models, operating production workloads on AWS (EC2/Lambda/RDS/DynamoDB/CloudWatch), and building reliable ETL/integration pipelines with idempotency, retries, and reconciliation.”