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Vetted REST APIs Professionals
Pre-screened and vetted.
Vinh Ly
Staff Software Engineer specializing in scalable full-stack FinTech systems
“Full-stack/backend engineer with recent hands-on experience building an AI-powered AML/KYC platform for financial institutions, spanning Go/Python backend services, real-time risk pipelines, and React/TypeScript analyst dashboards. Stands out for measurable compliance-tech impact: 94% fewer false positives, review times reduced from days to hours, and microservices processing 10,000+ transactions per second.”
Senior Full-Stack Engineer specializing in AI/ML platforms and cloud-native systems
Senior Software Engineer specializing in distributed systems, AI/ML, and healthcare platforms
Kaiqi Zhang
Senior Full-Stack Engineer specializing in cloud-native SaaS and AI applications
“ML/LLM engineer with hands-on experience shipping production RAG systems at Google Clinical Search and GenAI recommendation/summarization features in Meta Ads. Stands out for combining research-to-production execution with rigorous grounding, evaluation, safety checks, and reusable Python platform components that improved both reliability and team velocity.”
Hassaan Khan
Executive Technology Leader specializing in distributed systems, cloud infrastructure, and AI/ML
“Engineering leader/player-coach who built and validated a preference-based recommendation engine using clustering, including generating test data to evaluate how clusters evolve over time. Has SRE/DevOps experience and has owned production incidents end-to-end (logging-driven RCA and refactoring patterns that failed at large data scale). Emphasizes quality and platform stability via unit, integration, and load testing, and has managed performance via regular 1:1s and PIPs.”
Senior Software Engineer specializing in distributed systems and high-scale backend platforms
Senior Full-Stack Software Engineer specializing in AI platforms and cloud data systems
Staff Full-Stack Software Engineer specializing in cloud platforms and real-time health data
Mid-level AI/ML Engineer specializing in LLM training, RAG, and scalable inference
Staff Software Engineer specializing in ML infrastructure and data platforms
William Christerson
Senior Software Engineer specializing in backend systems and web applications
“Engineer with startup experience building AI-powered internal knowledge and support products from ambiguous early-stage concepts into production systems used by multiple teams. Particularly strong in end-to-end LLM product delivery across backend orchestration, retrieval, observability, security, and full-stack user experience, with clear impact on productivity and self-service adoption.”
Richard Jackson
Senior Machine Learning Engineer & Solution Architect specializing in cloud AI systems
“Backend/ML platform engineer with Google experience leading Python microservices for an AI-driven recommendation/retrieval system, including PyTorch inference and a retrieval-augmented generation workflow. Strong in production Kubernetes + GitOps (ArgoCD), real-time Kafka/Spark pipelines, and phased on-prem/legacy to AWS/GCP cloud migrations with reliability-focused rollout and rollback practices.”
Staff Machine Learning Scientist specializing in NLP, LLMs, and Generative AI
Senior Software Engineer specializing in backend APIs and cloud architecture
Senior Data Engineer specializing in cloud data platforms and real-time streaming
Staff Full-Stack Software Engineer specializing in cloud platforms and healthcare data pipelines
Senior Full-Stack Engineer specializing in AI and GenAI platforms
Nathan Telles
Staff Software Engineer specializing in distributed systems, cloud platforms, and AI services
“Meta engineer who owned end-to-end production systems for AI-enabled smart glasses, spanning React/TypeScript apps through Node/Java microservices on AWS EKS with Kafka/Postgres. Built and productionized a real-time RAG pipeline (LangChain + OpenAI + Elasticsearch) with rigorous guardrails (shadow/canary, fallbacks, monitoring), delivering major improvements in latency (~35–40%), error reduction (~30%), and engagement (reported +40% DAU).”