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Vetted AI Engineers
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and scalable inference
Mid-level AI Engineer specializing in GenAI, RAG, and multi-agent systems
Mid-level AI Engineer specializing in NLP, MLOps, and predictive analytics
Mid-level AI Engineer specializing in LLM systems, RAG, and MLOps
Senior Backend Engineer specializing in cloud-native microservices and computer vision
Entry-level AI Engineer specializing in LLM-powered backend systems
Mid-level AI/ML Engineer specializing in LLM fine-tuning, RAG, and computer vision
Senior Forward Deployed Engineer specializing in LLMs, RAG pipelines, and enterprise AI deployments
Mid-level AI/Software Engineer specializing in agentic AI and RAG systems
Senior Full-Stack Engineer specializing in cloud-native microservices, data engineering, and GenAI
Mid-level AI/ML Engineer specializing in LLMs, RAG, and GenAI pipelines
Mid-level Full-Stack GenAI/ML Engineer specializing in agentic AI and RAG systems
Senior AI Engineer specializing in LLM and generative AI production deployments
Sudheer koki
Mid-level AI/ML Engineer specializing in predictive modeling, data pipelines, and RAG systems
“Built and productionized an LLM-powered internal knowledge search system in a regulated environment, using embeddings/vector DB retrieval with strict grounding and confidence gating to reduce hallucinations. Reported ~45% accuracy improvement over keyword search and implemented end-to-end orchestration, monitoring, CI/CD, and incremental re-indexing to manage latency and data freshness while driving adoption with business stakeholders.”
Mid-level AI/ML Engineer specializing in financial risk, fraud detection, and NLP
Mid-level AI Engineer specializing in NLP, computer vision, and MLOps
Abdallah Al-Zubi
Senior Machine Learning Engineer specializing in NLP, computer vision, and edge AI
“AI/LLM engineer who built a production RAG-based Text2SQL engine using Qdrant, including creating the underlying business/DB documentation, generating a test dataset, and designing detailed SQL-quality metrics for validation. Also partnered with non-technical stakeholders on a speech recognition project to prioritize medical terminology, improving accuracy through targeted corpora, lookup-table correction, and fine-tuning with a modified loss function.”
shubham patil
Mid-level AI Engineer specializing in Generative AI, RAG systems, and fraud analytics
“Built and deployed a RAG-based student/faculty support chatbot at a university that answers from official syllabus/policy documents and now supports 4,000+ students while reducing repetitive support requests. Hands-on with LangChain, LangGraph, and CrewAI to orchestrate reliable agentic workflows, with a strong focus on testing/monitoring in production and cross-functional delivery (e.g., marketing analytics automation at Steve Madden).”