.png){kind=logo}
Vetted Hugging Face Transformers Professionals
Pre-screened and vetted.
Mid-level Applied AI/ML Engineer specializing in scalable generative model infrastructure
Mid-level AI Engineer specializing in Generative AI and RAG systems
Mid-level Full-Stack Java Developer specializing in cloud-native microservices and GenAI
Mid-level AI/ML Engineer specializing in NLP, GenAI, and fraud/risk analytics
Mid-level Machine Learning Engineer specializing in NLP and AWS data pipelines
Junior Machine Learning Engineer specializing in LLM infrastructure and inference optimization
Junior AI Engineer specializing in LLMs, enterprise automation, and bioinformatics
Mid-level Full-Stack Software Engineer specializing in cloud microservices and FinTech
Mid-level AI/ML Engineer specializing in Generative AI agents and workflow automation
Mid-level Machine Learning Engineer specializing in MLOps and Generative AI
Mid-level AI Engineer specializing in Computer Vision, NLP, and Generative AI
Mid-level AI/ML Engineer specializing in LLM fine-tuning and RAG for healthcare
Mid-level AI Engineer specializing in retail personalization and LLM-powered systems
Mid-level Machine Learning Engineer specializing in production ML, MLOps, and Generative AI
revanth polthi
Mid-level Data Scientist specializing in Generative AI and MLOps
“GenAI/LLM engineer with production experience at Allstate building an end-to-end document intelligence workflow for insurance operations—automating document intake, classification, and risk signal extraction. Emphasizes high-reliability design for regulated/high-stakes outputs using schema enforcement, confidence thresholds, validation rules, and human-in-the-loop routing, with metric-driven offline evaluation and production monitoring.”
Mid-level Backend Engineer specializing in cloud-native microservices and FinTech systems
VamshiKrishna Challa
Mid-level Data Scientist specializing in industrial IoT, predictive analytics, and generative AI
“ML/NLP engineer with Industrial IoT experience who built an end-to-end anomaly detection and GenAI explanation system: AWS (S3, PySpark, EC2/Lambda) pipelines feeding dashboards, plus transformer-embedding vector search to connect anomalies to noisy maintenance notes and past events. Demonstrated measurable impact (15% lift in defect detection; ~35% reduction in manual review; 35% fewer preprocessing errors) and strong productionization practices (orchestration, monitoring, rollback, data-quality controls).”