Vetted SOAP Professionals

Antonio Walker

Screened

Senior Technical Lead and Full-Stack Engineer specializing in cloud, AI, and enterprise platforms

Irvine, CA13y exp

“Engineering leader and player-coach who says he joined Freeing Returns during a transition from sales-led services to SaaS, architected the platform from the ground up, and helped hire a 10+ person team across engineering, product, and delivery. He also describes leading an AI-based fraud detection system on Salesforce with data lake and pipeline architecture, combining startup build-from-scratch execution with hands-on technical leadership.”

Teja Puvvula

Screened

Senior Full-Stack Software Engineer specializing in web apps, cloud, and data integrations

Florida, USA3y exp

“Full-stack engineer with strong production ownership across frontend, backend, and cloud ops. Led an AdTech “Status Centre” initiative at Beautiful Code that automated DSP integrations (XML/SOAP/REST) and reduced manual work by 90%+, using an event-driven architecture with retries and OTEL/Datadog observability. Also built and hosted a MERN app (Onegrad), load-tested with k6, and has experience running large migrations (~8k users) with validation and alerting.”

Charmi Dharmendrakumar Parmar

Screened

Mid-Level Software Engineer specializing in full-stack and cloud-native systems

Alpharetta, GA4y exp

“Backend/full-stack engineer who owned a cloud-native, AWS-based microservices backend for an HRIS product used by ~10,000 users, including onboarding and workflow orchestration. Strong production focus on event-driven architecture, idempotency/retries, observability, and developer-friendly API design (OpenAPI, versioning, JWT), plus hands-on Selenium automation for resilient checkout-style flows.”

“Led a government smart city deployment where success depended less on pure engineering and more on navigating policy, budget, and operational constraints. Built phased, cost-conscious systems combining data pipelines, on-prem AI, OCR, and human-in-the-loop workflows to deliver stable production outcomes and make ambiguous real-world data more controllable at scale.”