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Vetted Machine Learning Professionals
Pre-screened and vetted.
Executive Engineering & Product Leader specializing in digital commerce, customer platforms, and applied AI
Senior Machine Learning Engineer specializing in LLM inference and GPU infrastructure
Senior Software Engineer specializing in cloud-native microservices and observability
Director-level Software Development Manager specializing in AWS infrastructure and distributed systems
Senior Full-Stack Engineer specializing in Unity/C# and AI-driven VR/mobile healthcare systems
Executive product leader specializing in AI/ML, cloud infrastructure, and energy technology
Executive AI/ML Cloud Architect specializing in enterprise and humanitarian AI systems
Mid-level AI/ML Engineer specializing in FinTech risk and fraud systems
Mid-Level Software Development Engineer specializing in AWS serverless and ML/GenAI
Mid-level Solutions Engineer specializing in ads platforms and ML-driven marketing systems
Mid-level AI/ML Engineer specializing in LLM training, RAG, and low-latency inference
Senior Machine Learning Engineer specializing in GenAI, NLP, and recommendation systems
Director-level Strategy & Operations leader specializing in transportation and consulting
Director of Platform specializing in go-to-market and portfolio value creation
Senior AI Engineer specializing in machine learning, NLP, and generative AI
Executive technology leader specializing in cloud platforms, AI, and enterprise architecture
Sumer Joshi
Senior Full-Stack Engineer specializing in backend systems and AI applications
“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.”
Akshat Shah
Entry-level Software Engineer specializing in full-stack and AI systems
“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.”
David Besemer
Executive Technology Leader (CTO/VP Engineering) specializing in enterprise AI and data platforms
“Freelance CTO with a track record of founding and building early web products, including an award-winning baby website creation platform that was acquired by eStyle after gaining traction via retail/mail-order distribution and PR. Also built a sports event text-alert product, engineering around missing data APIs via a replaceable scraping layer.”
Yanis Liaskos
Executive Technology Leader (CTO/CPO) specializing in AI, robotics, and warehouse automation
“VP/CTO-level leader who builds globally distributed engineering organizations and operating systems (SAFe, governance, real-time metrics) to align product, engineering, and executives. Drove a security modernization initiative in a robotics context using Achilles and Polaris, remediating vulnerabilities and upgrading libraries to achieve a cyber certification that was ahead of industry norms.”
Rashi Agrawal
Mid-Level Full-Stack Software Engineer specializing in distributed systems and cloud-native microservices
“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
Intern Software Engineer specializing in AI agents, RAG pipelines, and semiconductor systems
“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.”