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Vetted Retrieval-Augmented Generation Professionals
Pre-screened and vetted.
Junior Machine Learning Engineer specializing in LLMs, data pipelines, and MLOps
Mid-level Machine Learning Engineer specializing in search ranking and NLP
Mid-level Machine Learning Engineer specializing in MLOps and cloud-native ML systems
Senior Data Scientist specializing in NLP, LLMs, and Generative AI automation
Senior Software Engineer specializing in AI/ML tooling and data platforms
Senior AI/ML Software Engineer specializing in LLMs, NLP, and scalable ML platforms
Senior Data Scientist specializing in Generative AI and LLM evaluation
Staff-level Data Scientist specializing in LLMs, NLP, and product experimentation
Intern AI/ML Engineer specializing in LLM agents, RAG, and low-latency systems
Senior Applied Scientist specializing in LLMs, GenAI systems, and AutoML
Mid-level Machine Learning Engineer specializing in LLMs, ranking, and scalable ML systems
Senior Full-Stack Engineer specializing in Ads and FinTech platforms
Staff Machine Learning Engineer specializing in LLMs and cloud-native AI platforms
Senior Software Engineer specializing in Python, cloud microservices, and conversational AI
Kusumita Dasgupta
Director-level Engineering Leader specializing in data platforms, cloud systems, and LLM products
“Engineering leader/player-coach with recent hands-on work delivering an agentic AI MVP on Amazon Bedrock (conversational UI + supervisor agent routing between internal knowledge and external sources). Previously drove large-scale data platform cost optimization at Twitter, saving ~$3M–$5M annually, and has owned production incidents end-to-end with a focus on analytics/monitoring improvements and team coaching.”
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 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 AI/ML Engineer specializing in LLMs, RAG, and distributed MLOps