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Vetted Azure Machine Learning Professionals
Pre-screened and vetted.
Mid-level Data Analyst/Data Scientist specializing in ML, dashboards, and predictive analytics
Mid-level AI/ML Engineer specializing in NLP, GenAI, and fraud/risk analytics
Mid-level Machine Learning Engineer specializing in Generative AI, LLMs, and MLOps
Mid-level AI/ML Engineer specializing in LLMs, NLP, and MLOps
Mid-level Machine Learning Engineer specializing in MLOps, fraud detection, and data security
Mid-level AI/ML Engineer specializing in Generative AI agents and workflow automation
Mid-level Machine Learning Engineer specializing in MLOps and Generative AI
Senior Machine Learning Software Engineer specializing in Azure enterprise AI
Mid-level AI/ML Engineer specializing in LLM fine-tuning and RAG for healthcare
Junior AI Engineer specializing in distributed ML pipelines and time-series forecasting
Mid-level AI Engineer specializing in retail personalization and LLM-powered systems
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered analytics
Mid-level Machine Learning Engineer specializing in production ML, MLOps, and Generative AI
Mid-level Full-Stack .NET Developer specializing in Angular, Azure, and AI integrations
Yashwanth Penugonda
Mid-level AI/ML Engineer specializing in LLMs, RAG, and production GenAI systems
“Built and deployed a production LLM-powered RAG knowledge system to unify operational/policy information across PDFs, wikis, and databases, emphasizing auditability and low-latency/cost performance. Improved answer relevance at scale by moving from pure vector search to hybrid retrieval with metadata filtering and reranking, and partnered closely with healthcare operations/compliance to define acceptance criteria and human-in-the-loop guardrails.”
Divya Narahari
Mid-level Software Engineer specializing in AI/ML systems and backend platforms
“New grad focused on AI systems and agent-based development, with hands-on experience using LLMs as a coding partner and building RAG-based document processing workflows. Stands out for practical experimentation with semantic chunking, retrieval optimization, and multi-agent architectures, including redesigning a RAG workflow by adding a reasoning agent to improve response accuracy and reliability.”
Mid-level Data Scientist & AI Engineer specializing in NLP, computer vision, and MLOps