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Vetted Test Automation Professionals
Pre-screened and vetted.
Senior Data Engineer specializing in cloud data platforms and scalable ETL pipelines
Mid-level Software Development Engineer specializing in AWS cloud services and distributed systems
Director-level Engineering Leader specializing in platform, data, and cloud systems
Executive Technology & Product Leader specializing in Video Games, Web3, and AI/ML
Senior Backend Engineer specializing in cloud-native Python microservices
Mid-level Machine Learning Research Engineer specializing in foundation models and GenAI
Engineering Manager specializing in cloud-native platforms across AdTech and FinTech
Intern Software Engineer specializing in cloud, DevOps, and LLM-powered tooling
Senior QA Lead specializing in video game testing (mobile, PC, console)
Senior Full-Stack Engineer specializing in cloud-native SaaS platforms
Junior Software Engineer specializing in full-stack, cloud, and AI systems
Senior Full-Stack Engineer specializing in cloud-native enterprise applications and ServiceNow ITSM
Director of Engineering specializing in cloud, microservices, and data-intensive platforms
Executive IT & Software Development Leader specializing in cloud-native transformation and trading platforms
Senior Data Engineer specializing in cloud data platforms and big data pipelines
Thomas Majchrowski
Executive Technology Leader (CTO) specializing in SaaS, AI platforms, and M&A integration
“Entrepreneurial builder who created a SaaS system for insurance sales and developed in-house sales/marketing platforms, prioritizing a robust backend that drove over $500k in immediate sales before expanding features. Emphasizes lean, high-impact execution with strong focus on optimization, barrier management, and contingency planning.”
Hari Kiran Reddy Rommala
Mid-level Full-Stack Software Engineer specializing in cloud and data platforms
“Full-stack engineer with experience spanning Amazon IMDb and Northeastern’s NeuroJSON portal, combining consumer product work with complex scientific data applications. Built IMDb’s streaming providers feature—described as the company’s most impactful feature of 2023—and has hands-on experience with React/Angular, GraphQL, AWS, Python services, and production monitoring.”
Chitra Uppalapati
Entry-level Software Engineer specializing in full-stack and machine learning applications
“Built production Python data integrations and dashboard automation for incident analytics, with a strong focus on data quality, observability, and reliability for leadership-facing reporting. Also translated an ambiguous manual creator evaluation process at startup Spring into an automated predictive scoring feature, showing a blend of backend data engineering, test automation, and cross-functional product thinking.”
Jonas Shuai
Mid-level Full-Stack Software Engineer specializing in cloud, microservices, and React/Java
“Software engineer with experience at PayPal and JPMC building large-scale onboarding/account setup systems using React/TypeScript with Spring Boot/Node microservices and Kafka. Also built an Ignition-based SCADA monitoring tool at Mainspring Energy that became the default for manufacturing/test engineers by aggregating real-time telemetry and historical test data.”
Shanmukha Koganti
Mid-level AI/ML Engineer specializing in recommender systems and edge computer vision
“ML/AI engineer with production experience at Shopify and Intel, building a deep learning product ranking system that lifted add-to-cart ~14% and serving real-time similarity search via FAISS+Redis under <20ms latency at massive scale. Also deployed computer vision models to 100+ retail edge locations using Docker/Ansible/k3s with zero-downtime rollouts, and applies strong MLOps practices (A/B testing, canary/shadow, observability) plus performance optimization (OpenVINO, INT8).”
Rakesh Munaga
Mid-level Full-Stack Engineer specializing in AI and FinTech platforms
“Full-stack engineer building real-time internal banking operations dashboards (Java/Spring Boot microservices + React/TypeScript) with Kafka-based streaming and post-launch performance optimizations. Also shipped a production internal AI support assistant using RAG (Confluence/PDF/support docs ingestion, embeddings + vector DB retrieval) with guardrails, evaluation loops, and observability to reduce hallucinations and prevent regressions.”