.png){kind=logo}
Vetted Model Fine-tuning Professionals
Pre-screened and vetted.
Senior AI/ML Engineer specializing in Python, LLMs, and agentic AI on cloud platforms
Junior Software Engineer specializing in test automation and AI/ML tooling
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level AI/ML Engineer specializing in fraud detection and real-time ML systems
Junior Machine Learning Engineer specializing in LLM training and high-performance inference
Mid-level AI/ML Engineer specializing in Generative AI, RAG, and multi-agent LLM systems
Senior AI/ML Engineer specializing in LLMs, RAG, and high-performance systems
Mid-level AI Engineer & Data Scientist specializing in Generative AI, NLP, and Cloud ML
Mid-level Data Scientist & AI/ML Engineer specializing in GenAI and LLM-driven enterprise systems
Mid-Level Generative AI Engineer specializing in LLM apps, RAG, and cloud deployment
Senior Software Engineer specializing in GenAI and full-stack enterprise applications
Senior Machine Learning Engineer specializing in Generative AI and LLM systems
Senior Machine Learning Engineer specializing in GenAI, LLMs, and MLOps
Mid-level AI Engineer specializing in production LLM, RAG, and agentic AI systems
vedavathi thumula
Mid-level GenAI/ML Engineer specializing in agentic AI and RAG systems
“Backend/platform engineer who has owned a Python/FastAPI results API and deployed it on Kubernetes with Helm and GitHub Actions-driven CI/CD. Demonstrates strong production operations mindset across performance tuning, monitoring, safe rollouts/rollbacks, and phased migrations, plus hands-on Kafka streaming experience focused on ordering and idempotency.”
Yurong Luo
Senior Data Scientist/ML Engineer specializing in scalable ML and LLM systems
“Built and deployed an end-to-end product that brings a research-paper approach into production for large-scale time-series clustering, with attention to partitioning, latency, and scalability. Also designed a Python-based backend validation service (comparing outputs to database ground truths) and handled production reliability issues by reproducing dataset-specific crashes and hardening corner-case behavior with client-friendly errors.”