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Vetted AI & Machine Learning Professionals
Pre-screened and vetted.
Popular Titles
Senior Full-Stack Engineer specializing in AI/GenAI and cloud-native platforms
Senior AI/ML Engineer specializing in computer vision, NLP, and real-time forecasting
Mid-level Machine Learning Engineer specializing in real-time recommender systems and MLOps
Intern Machine Learning Engineer specializing in systems, kernels, and GPU computing
Intern Machine Learning Engineer specializing in LLM systems and recommendation/search
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multi-agent systems
Mid-level AI/ML Engineer specializing in NLP, computer vision, and recommender systems
Mid-level AI/ML Engineer specializing in RAG, NLP, and MLOps
Mid AI/ML Engineer specializing in LLMs, RAG, and multimodal systems
Senior Machine Learning Scientist specializing in LLMs, RAG, and health AI
Parth Vadera
Senior AI/ML Data Scientist specializing in recommender systems, LLMs, and MLOps
“ML/NLP leader with 12+ years of impact across LinkedIn, TikTok, and Levi's, building and productionizing multimodal recommendation and embedding-based search systems. Deep experience in entity resolution, vector retrieval, and rigorous evaluation, with cloud-native deployment/monitoring (MLflow, Airflow, SageMaker/Lambda, Azure ML, Kubernetes) and demonstrated double-digit relevance gains at millions-of-users scale.”
Harish Kasu
Mid-level AI/ML Engineer specializing in Generative AI, RAG, and MLOps
“AI/LLM engineer with production experience at NVIDIA and Microsoft, including building a RAG-based enterprise knowledge assistant that improved accuracy by 42% and scaled to thousands of queries. Deep in inference optimization (TensorRT-LLM, Triton, quantization, speculative decoding) and MLOps/observability (Prometheus/Grafana, MLflow, LangSmith), plus orchestration with Kubeflow/Airflow across multi-cloud.”
Yishi Wang
Junior Machine Learning & Data Science professional specializing in LLMs and analytics
“Amazon internship experience building production GenAI analytics for the returns organization: a multi-agent LLM+RAG system that let analysts query multiple heterogeneous data sources in natural language without hand-written SQL. Also built and operationalized four Apache Airflow DAGs for large-scale ETL, emphasizing observability and freshness-aware metadata to keep outputs accurate and up to date.”
Vinay Ramrupe
Mid-level AI/ML Engineer specializing in LLM and enterprise generative AI
“ML/AI engineer focused on taking LLM systems from experimentation to reliable production, including enterprise copilot and RAG-based knowledge retrieval use cases. Stands out for combining data pipelines, model training, inference optimization, automated evaluation, and safety guardrails, with cited impact including 20% throughput gains and 30% less manual evaluation effort.”
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and scalable GPU inference
Intern Machine Learning Engineer specializing in LLMs, RAG, and model quantization
Principal Machine Learning Scientist specializing in GenAI, LLMs, and RAG