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Vetted Hugging Face Transformers Professionals
Pre-screened and vetted.
Mid-level Data Analyst specializing in ML, AI, and data visualization
Mid-level Python Developer specializing in APIs, microservices, and data-driven e-commerce systems
Director-level Aerospace & Aerial Robotics Engineer specializing in eVTOL and drone-enabled agribusiness
Mid-Level Generative AI Engineer specializing in LLM apps, RAG, and cloud deployment
Senior Data Engineer specializing in AWS cloud data platforms and streaming analytics
Mid-level Data Scientist / ML Engineer specializing in NLP, GenAI, and cloud ML deployment
Senior Data Scientist and AI Engineer specializing in NLP, LLMs, and MLOps
Senior Machine Learning Engineer specializing in GenAI, LLMs, and MLOps
Senior Machine Learning Engineer specializing in GenAI, LLMs, and MLOps
Senior Data Scientist specializing in NLP, MLOps, and cloud ML platforms
Senior AI Python Engineer specializing in Generative AI and MLOps
Senior Full-Stack Developer specializing in cloud microservices and enterprise applications
Saikrishna Paila
Junior AI Engineer specializing in RAG pipelines and agentic AI systems
“Built and shipped production RAG/agentic systems in high-stakes domains (biomedical and legal), including an enterprise biomedical document retrieval platform over ~10k scientific docs and a multilingual African-law assistant at the World Bank. Deep hands-on experience with LangChain/LangGraph/LlamaIndex and evaluation tooling (LLM-as-a-judge, safety/hallucination detection), with measurable gains in retrieval quality and hallucination reduction.”
Pravallika Kilari
Mid-level AI/ML Engineer specializing in NLP, GenAI, and MLOps in healthcare and finance
“AI/ML engineer with CVS Health experience deploying production LLM systems in regulated healthcare settings, including a large-scale RAG solution (1M+ documents) built for compliance-grade, auditable policy/regulatory Q&A with strong anti-hallucination controls. Also delivered an NLP summarization system for physician notes/case narratives by partnering closely with non-technical care operations stakeholders and iterating via prototypes, dashboards, and feedback loops.”