Vetted Jenkins Professionals

“Built and operated a production warehouse metadata collection platform at Sigma Computing, integrating Go/gRPC services with a TypeScript backend and MySQL, with strong emphasis on idempotency, retries, bounded-concurrency job queues, and Datadog-based observability. Also created Kurral (kurral.com), an AI agent runtime security and observability/governance SDK/proxy concept, iterating via pilot-customer feedback and market research; targeting founding engineer roles with $180–200k base and ~2–5% equity.”

Sumer Joshi

Screened ReferencesStrong rec.

Senior Full-Stack Engineer specializing in backend systems and AI applications

Remote13y exp

“Candidate is deeply focused on AI-native software development, using a deliberate planner/implementer agent workflow with tools like Cursor, Claude, and Kimi. They also built a personal project called Config Proctor, an AI-agent-driven Terraform/AWS self-healing system that identifies infrastructure configuration gaps and proposes fixes.”

Mike Henniger

Screened ReferencesStrong rec.

Staff Software Engineer specializing in scalable backend and full-stack web systems

Burlingame, CA32y exp

“Frontend-leaning full-stack product engineer with IMVU experience who designed both user-facing flows and the production telemetry behind them. Most notably, they built a JavaScript crash detection system using signature-based aggregation and Redis that helped reduce crash volume from millions per day to tens of thousands, and they pair that reliability mindset with reusable UI architecture and onboarding design for consumer social products.”

“Frontend-leaning full-stack engineer who described owning an artist search and detail experience across UI, backend integrations, and data modeling. They show practical strength in scalable React architecture, TypeScript safety, and performance tuning, with a product-minded approach to shipping 0→1 features quickly and iterating after launch.”

“Backend/AI engineer who has built a rule-service platform on AWS and evolved it into an agentic RAG system using LangChain, ReAct, tool calling, and LLM-as-judge review. Notable for combining heavy AI-assisted development with production safeguards like manual CR, CloudWatch monitoring, fallback strategies, benchmark testing, and user-feedback-driven model improvement.”

“Player-coach engineer/lead on a high-scale research integrity platform ("Lighthouse") that flags fraud/manipulation signals across ~3M academic manuscripts per year. Owns architecture decisions (ADRs), implements across Go/Java/React services, and introduced NLP (SciBERT embeddings + human-in-the-loop) to assess out-of-context citations while also handling production incidents with a data-consistency-first approach.”

Rashi Agrawal

Screened

Mid-Level Full-Stack Software Engineer specializing in distributed systems and cloud-native microservices

Novi, MI4y exp

“Backend engineer (4 years) who built an end-to-end Python backend for a patent-pending in-car massager/heater system, including GraphQL data modeling and Bluetooth integration with an ESP32 microcontroller (reverse engineered a niche protocol). Also has strong platform experience: on-prem Kubernetes/CI-CD (Jenkins/GitLab, exploring ArgoCD GitOps), Terraform-based infra workflows, a RabbitMQ messaging library used across microservices, and an on-prem migration of ~30 critical applications with rollback/parallel-run strategy.”

Yuan-Hsuan Wen

Screened

Intern Software Engineer specializing in AI agents, RAG pipelines, and semiconductor systems

Taipei, Taiwan3y exp

“Built a web-based interface that connects an internal bug system to an LLM for initial debugging and issue classification, aiming to boost QA and software engineer efficiency while balancing latency and accuracy. Worked as a one-person project and managed constraints like limited hardware and difficulty extracting team debugging context, relying on manager communication and rapid modeling to validate direction.”

“Full-stack engineer at Vanguard who architected and shipped a production AI onboarding chatbot using React, Python, LangChain, RAG, and AWS Bedrock, reaching 50,000+ prospects in two months and reducing drop-off by 26%. Particularly compelling for teams building regulated AI products: they combine hands-on full-stack delivery with guardrails, observability, and experimentation, and also build consumer AI products on the side in endurance coaching.”