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Vetted Model Evaluation Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in financial risk, fraud detection, and NLP
Mid-level Software Engineer specializing in backend systems, data pipelines, and GenAI automation
Mid-level AI/ML Engineer specializing in LLM fine-tuning, NLP, and MLOps
Lead Data Scientist specializing in Generative AI, LLM systems, and MLOps
Mid-level Full-Stack Developer specializing in FinTech and cloud microservices
Mid-level AI/ML Engineer specializing in scalable ML systems and cloud MLOps
Mid-level Machine Learning Engineer specializing in NLP, LLMs, and deep learning
Mid-level AI Backend Engineer specializing in LLM applications and scalable ML services
Mid-level Software Engineer specializing in backend systems and LLM applications
Mid-level AI Backend Engineer specializing in LLM applications and scalable ML services
Mid-level Software Engineer specializing in backend systems, cloud microservices, and AI-driven automation
Mid-level AI/ML Data Engineer specializing in analytics, ML pipelines, and LLM applications
Mid-level AI Engineer specializing in LLM orchestration and production AI systems
Syed Daim Ali
Intern Software Engineer specializing in FinTech and AI platforms
“Systems-focused engineer who built an OS kernel with multithreading, priority scheduling, system calls, and synchronization primitives, and debugged race conditions end-to-end. While not yet hands-on with ROS/SLAM, they clearly connect low-level concurrency and scheduling decisions to deterministic, reliable robotics-style real-time workloads.”
Max Matkovski
Junior Machine Learning Engineer specializing in data pipelines and applied AI
“Built a production AI agent for phishing fraud detection using n8n orchestration, Claude (Sonnet 4/MCP), VirusTotal, and JavaScript formatting to generate and deliver email-based reports via Gmail. Has experience evaluating detection accuracy against known examples, iterating via feedback, and presenting AI solutions to non-technical teams.”
“ML/LLM practitioner with experience at Truveta building an LLM-based evaluation framework; identified non-overlapping evaluator failure modes and proposed an ensemble approach that enabled scaling training data and drove ~5% performance gains across multiple internal projects. Strong focus on robustness to distribution shift (augmentation/domain adaptation/meta-learning) and production reliability via monitoring, drift detection, and safe fallbacks.”