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Vetted System Design Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in RAG systems and cloud data platforms
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Senior Software Engineer specializing in payments platforms and distributed systems
Senior Software Engineer specializing in backend microservices and real-time data pipelines
Mid-level Full-Stack Engineer specializing in cloud-native systems and event-driven architectures
Senior Backend Software Engineer specializing in AI-driven automation
Mid-level Software Engineer specializing in AI applications and distributed backend systems
Senior Software Engineer specializing in cloud-native distributed systems
Staff Software Engineer specializing in cloud, networking, and distributed systems
Mid-level Software Development Engineer specializing in AWS incident management and AI analytics
Mid-level AI/ML Data Engineer specializing in data pipelines, MLOps, and LLM/RAG systems
Staff/Principal Software Engineer specializing in embedded systems, networking, and ML
Executive technology leader specializing in AI, LLMs, search, and platform engineering
Junior Machine Learning Engineer specializing in FinTech and scalable backend systems
Senior Full-Stack Software Engineer specializing in scalable microservices and cloud platforms
Mid-Level Software Development Engineer specializing in AWS serverless and backend APIs
Mid-Level Full-Stack Python Engineer specializing in AI-powered web apps and cloud-native systems
Mid-level Software Development Engineer specializing in backend systems and ML platforms
Mid-Level Software Engineer specializing in cloud-native backend and distributed systems
Mid-level Software Engineer specializing in backend systems, microservices, and AI search
Senior AI Infrastructure Engineer specializing in LLM systems and real-time ML platforms
Shalin Bhavsar
Mid-level Software Engineer specializing in cloud backend and distributed systems
“Built a production GenAI support agent at Amazon for FBA on-call operations, using Bedrock, Lambda, RAG, and confidence-based human fallback to safely automate ticket triage. The system materially reduced ticket volume and manual workload while improving MTTR, showing strong depth in reliable LLM agent architecture under real operational constraints.”