.png){kind=logo}
Vetted Data Pipelines Professionals
Pre-screened and vetted.
Senior Software Engineer specializing in Unity, VR/XR, and AI-driven systems
Senior Applied Machine Learning Engineer specializing in FinTech & E-commerce
Senior Full-Stack Software Engineer specializing in ML platforms and privacy-preserving ads
Senior AI/ML Engineer specializing in Generative AI and cloud-native platforms
Senior Machine Learning Engineer specializing in Generative AI and NLP
Staff Full-Stack Software Engineer specializing in AI-powered platforms and scalable backend systems
Junior Software Engineer specializing in backend systems and security
Mid-level AI/ML Engineer specializing in Generative AI and multilingual NLP
Jeffrey Xuzhang
Junior Software Engineer specializing in full-stack and cloud systems
“Worked on an AWS DynamoDB Journal team project building internal operator dashboards end-to-end, creating Java/Spring Boot APIs and integrating them into a Spring Boot/Thymeleaf/JavaScript UI to speed up debugging workflows. Has experience with data-heavy web apps and performance techniques (load balancing, caching, pagination, compression) plus hands-on debugging across unit/integration/E2E tests; also maintained and enhanced a React website at Global Spark.”
Senior Backend Software Engineer specializing in distributed commerce and billing systems
Cody Christ
Senior Software Engineer specializing in AI, full-stack platforms, and real-time systems
“Built end-to-end AI analytics experiences spanning React/TypeScript, serverless APIs, and Postgres, with a strong focus on streaming UX, observability, and reliability. Stands out for turning ambiguous AI product ideas into shippable MVPs, then abstracting repeated patterns into reusable orchestration and multi-tenant configuration systems that improved speed, consistency, and maintainability.”
Andrew Stewart
Senior Software Engineer specializing in Healthcare AI and FinTech platforms
“Google Health engineer who owned and shipped an AI-powered clinical insights dashboard and NLP clinical note extraction service end-to-end (React/Next.js frontend; Python/Node microservices on GKE; TensorFlow transformers; BigQuery analytics). Demonstrated strong production rigor (CI/CD, testing, observability, guardrails for sensitive data) and delivered measurable outcomes including 30% faster diagnostics, 40% less manual documentation, 15% higher adoption, and 25% lower ops costs.”
Senior Full-Stack Engineer specializing in microservices, data pipelines, and cloud platforms
Senior AI/ML Engineer specializing in LLM systems and FinTech platforms
Senior Software Engineer specializing in cloud-native microservices and large-scale backend systems
Senior Full-Stack AI Engineer specializing in LLM/RAG and production ML platforms
Jian Guo
Executive Data & AI Leader specializing in enterprise data platforms and analytics
“Early-stage founder building a service business targeting small clinics, already with one client. Identified the opportunity by helping a family member and then validating needs through direct client conversations; uses AI (including AI agents) for content generation and plans deeper workflow automation to scale cost-effectively.”
Brian Gomez
Senior Machine Learning Engineer specializing in AI/ML, NLP, and computer vision
“McKinsey & Company ML/NLP practitioner who builds production-grade AI systems across sectors (notably healthcare and finance), including RAG/LLM solutions, entity resolution pipelines, and embedding-powered search with vector databases. Demonstrated measurable impact (40% reduction in data duplication) and strong MLOps/data workflow practices (Airflow, MLflow, Spark, AWS/GCP, Prometheus, CI/CD).”
Sahitya Thota
Staff/Tech Lead Software Engineer specializing in identity, data platforms, and cloud systems
“Engineering leader/player-coach who built and owned end-to-end sales-facing data products, including a 360° advertiser insights platform and a sales AI agent for natural-language access to insights. Demonstrated strong architecture and reliability chops (event-driven redesign to eliminate Hive-query throttling; batching/caching to reduce fan-out) plus incident ownership around ads attribution consistency. Also has 0→1 experience building graph-based recommendation/matching systems with explainability and tight user feedback loops.”