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Vetted Amazon SageMaker Professionals
Pre-screened and vetted.
Senior Software Engineer specializing in cloud-native backend, ETL, and AI/ML on AWS
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and RAG for financial services
Mid-level Machine Learning Engineer specializing in NLP, recommender systems, and MLOps
Senior Machine Learning Engineer specializing in Generative AI and NLP
Senior Full-Stack Software Engineer specializing in cloud microservices and GenAI
Mid-level AI/ML Engineer specializing in NLP, MLOps, and financial risk & fraud analytics
Senior AI/ML Engineer specializing in GenAI, MLOps, and healthcare analytics
Senior Data Engineer specializing in Cloud Data Platforms and Generative AI
Mid-level AI/ML Product & Solutions Specialist specializing in GenAI and MLOps
Principal Data Scientist specializing in AI/ML forecasting and MLOps
Mid-level AI/ML Engineer specializing in forecasting, MLOps, and generative AI
Principal Data Scientist specializing in LLMs, RAG, and enterprise AI products
Senior AI/ML Engineer specializing in LLMs and enterprise conversational AI
Johnnie Sanders
Executive AI Architect specializing in enterprise cloud and FinTech solutions
“Candidate brings an operator-to-founder profile with leadership experience in IT and Business Systems and a strong grasp of how ideas become venture-backable products. They speak fluently about startup evaluation criteria such as TAM, technical defensibility, speed to scale, and AI differentiation, and appear especially motivated by building solutions end-to-end in startup or venture studio environments.”
NEHA KOLAN
Mid-Level Software Engineer specializing in microservices and cloud data pipelines
“Full-stack engineer with end-to-end ownership across React/TypeScript frontends, Spring Boot/Node microservices, and production ops on Docker/Kubernetes and AWS (ECS/CloudWatch). Built real-time healthcare eligibility and analytics systems at Cigna and an early-stage seller onboarding platform at Flipkart, driving measurable performance gains (35–40% latency/throughput improvements) through event-driven Kafka pipelines, Redis caching, and strong reliability/observability practices.”