Vetted Software Engineers in the Portland Metro

“Backend/data engineer with experience at Nike building high-volume order orchestration and validation APIs using FastAPI microservices on AWS EKS with Kafka, Redis, and Postgres. Strong in production reliability (timeouts/retries/idempotency), GitOps (Argo CD) + Terraform deployments, and data pipelines (AWS Glue/S3), with hands-on incident ownership and legacy modernization into API-driven services.”

Daksh Jain

Screened

Mid-Level Software Development Engineer specializing in AWS streaming media platforms

Portland, OR3y exp

“Full-stack engineer with hands-on Next.js App Router + TypeScript experience (built a RateMyProfessor-style platform end-to-end using RSC, dynamic routing, debounced search, and cache invalidation). Also has AWS backend depth—built a Step Functions-based wave rollout/feature access control framework for MediaPackage V2 with idempotency, retries, rollback, and ongoing correctness reconciliation.”

“Full-stack engineer who built and scaled an AI-powered web product (React/Next.js + TypeScript/NestJS) with MongoDB, Redis, and RabbitMQ. Strong in rapid iteration while maintaining production quality—uses versioned APIs, feature flags, CI/CD, and observability (correlation IDs/structured logs) to ship frequently and debug distributed workflows. Also created an internal operations dashboard for real-time visibility and control of background jobs/AI workflows that was adopted quickly by ops and product teams.”

“Serial entrepreneur with 30+ years of professional software engineering experience who co-founded Food Truck Stars, initially a food-truck search engine and later pivoted into a catering marketplace to overcome adoption barriers. Validated the model by prototyping at car enthusiast events across the U.S. and turning a profit, using hands-on customer discovery with low-tech, time-constrained operators.”

“Built and deployed an LLM product called "Content Craft" combining BART-based summarization with a RAG Q&A chatbot using LangChain, embeddings, and a vector database. Has hands-on MLOps experience containerizing and serving models with FastAPI and running them on Kubernetes with monitoring, self-healing, and autoscaling, and has practical experience reducing hallucinations through structured prompting.”

“Full-stack engineer working on a medical practice SaaS, who owned an end-to-end charge entry module handling patient charges/payment workflows and complex client-side data transformations. Also built a fintech rating platform with real-time WebSocket streaming and TradingView-based charting, and has 10 years of university experience mentoring student "startup" capstone projects under tight deadlines.”