Vetted Full Stack Developers

“Backend/full-stack engineer who has built AI-powered search and workflow systems in production, including a semantic resume-matching platform for recruiters and internal security data dashboards at ReliaQuest. Stands out for combining modern AI tooling with pragmatic reliability, performance tuning, and strong product intuition in ambiguous environments.”

“Backend/data engineer who has maintained high-traffic FastAPI microservices and delivered a hybrid AWS serverless+containers platform using Terraform and GitHub Actions, with secrets managed via Secrets Manager/SSM. Also led modernization of a mission-critical 10,000+ line SAS financial reporting engine into Python microservices and built AWS Glue ETL pipelines feeding a centralized data lake.”

“Open-source JavaScript library contributor/maintainer focused on performance and usability—uses profiling and user feedback to optimize large-dataset processing and modernize abstractions. Refactored a nested-callback event handling system into an observer-pattern dispatcher with batched event queues, reducing CPU usage and improving maintainability; also handles community-reported crashes by reproducing issues, fixing memory leaks, and updating docs.”

Siddhanth Erramaraju

Screened

Junior Full-Stack Developer specializing in MERN and AWS

Birmingham, AL3y exp

“Backend engineer focused on Python/Flask APIs and cloud-native delivery: builds stateless services with JWT auth, validation, and scalable deployment on Kubernetes using a GitOps workflow (ArgoCD-style) with easy rollbacks. Has also implemented Kafka-based real-time event pipelines and supported phased hybrid cloud/on-prem migrations with parallel runs and controlled cutovers.”

Apurva Banka

Screened

Mid-level Full-Stack & AI Engineer specializing in cloud, data platforms, and LLM automation

Houston, TX5y exp

“Software engineer/product builder who has owned an agentic affiliate lead-gen platform end-to-end (Django + React/TypeScript) and deployed it on Kubernetes in anticipation of 10x user growth from ~5K DAUs. Also has healthcare claims microservices experience using Kafka, including hands-on performance tuning to address consumer lag and broker pressure, and built an internal downtime alerting tool adopted across the organization.”

Teage Johnson

Screened

Junior Full-Stack Engineer specializing in FinTech and machine learning

California, USA3y exp

“Software engineer at early-stage startup Cari with hands-on experience shipping AI-enabled production workflows, including an LLM chatbot for a micro-transit platform and an automated image-processing pipeline integrated with Claude. Stands out for combining practical agent reliability patterns—schema validation, fallbacks, caching, and idempotency—with strong ML evaluation instincts and experience cleaning messy operational invoice data.”

“AI/ML engineer at MRI Software focused on taking LLM and RAG systems from prototype to reliable production. Notable work includes an AI automation system for migrating 1200+ legacy pages with 75-80% manual effort reduction, plus enterprise document-querying and reusable Python LLM infrastructure that cut lookup time by 70% and improved team velocity by 30-40%.”

“Full-stack engineer with end-to-end ownership of a production customer plan activation and account management flow at T-Mobile, spanning Java/Spring Boot APIs, React frontend, and Docker-based CI/CD deployments. Demonstrated performance/scalability work (query optimization, indexing, caching) and measured success via improved retrieval speed and reduced support tickets.”

Harika M

Screened

Mid-level Full-Stack Developer specializing in FinTech and enterprise platforms

Plano, TX6y exp

“Engineer with a pragmatic, production-focused approach to AI-assisted development, using tools like Copilot and ChatGPT to accelerate coding while maintaining strict validation for correctness, security, and performance. Particularly notable for building a multi-agent incident-resolution workflow for a financial platform, with specialized agents for log analysis, root cause identification, fix suggestions, and test generation.”

rhema junapudi

Screened

Mid-level Full-Stack Developer specializing in cloud-native healthcare and insurance platforms

Albany, OR3y exp

“Full-stack engineer with healthcare and claims-domain experience who has shipped AI-assisted documentation and fraud-scoring systems using Angular, ASP.NET Core, OpenAI APIs, and ML tooling. Stands out for building compliance-aware, traceable agentic architectures with graceful degradation, and for a nuanced understanding of AI failure modes in production.”

“Early-career engineer who built and launched a zero-to-one AI-driven approval workflow at SDSU that is used daily by roughly 2,000 university users. They owned the system end-to-end—from FastAPI/PostgreSQL backend to React UI—and showed strong judgment around LLM reliability, using a two-step pipeline, validation checks, and human-review fallbacks to cut manual processing time by about 80%.”

“Full-stack engineer with hands-on ownership of a production telemedicine dashboard at Cleveland Clinic, spanning React/TypeScript frontend, Spring Boot and Node.js backend services, and AWS/Kubernetes infrastructure. Stands out for real-time systems expertise, measurable performance improvements in a healthcare setting, and a clear interest in applying that backend/cloud foundation to AI agent systems.”

“Full-stack engineer who shipped a production "Financial Insight" assistant dashboard in Next.js App Router/TypeScript, integrating a RAG pipeline (embeddings + ChromaDB + LLM) via route handlers and owning post-launch performance (latency, token cost, retrieval relevance). Also built/optimized Postgres-backed workflows for an outbound dialer and callback routing engine handling ~10,000 daily contacts, validating query performance with EXPLAIN (ANALYZE, BUFFERS).”

“Full-stack engineer at Citi who stepped into an iOS role early in their tenure, quickly learned ReactorKit, and shipped a customer transaction memo feature within two weeks that reached thousands of users. They also contributed to major performance gains, helping reduce page load times from 30 seconds to 3 seconds across hybrid app experiences by optimizing API behavior and cross-stack implementation.”

“Backend/data platform engineer who owned end-to-end production services for a fleet analytics/GenAI platform, spanning FastAPI microservices on Kubernetes and AWS (EKS + Lambda) event-driven workloads. Strong in reliability/observability (OpenTelemetry, circuit breakers, idempotency), data pipelines (Glue/Airflow/Snowflake), and measurable performance/cost wins (SQL 10s to <800ms P95; ~30% compute cost reduction).”