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Vetted Hyperparameter Tuning Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in fraud detection, MLOps, and LLM/RAG applications
Mid-level AI/ML Engineer specializing in predictive modeling and NLP for healthcare and FinTech
Mid-level AI & Machine Learning Engineer specializing in MLOps and NLP
Mid-level AI/ML Engineer specializing in GenAI, NLP, and AWS MLOps
Mid-level Machine Learning Engineer specializing in production MLOps and healthcare analytics
Senior AI/ML Engineer specializing in Agentic AI, RAG, and LLM systems
Junior Full-Stack Software Engineer specializing in web, cloud, and Android development
Mid-level AI/ML Engineer specializing in NLP, GenAI, and fraud/risk analytics
Mid-level Robotics Engineer specializing in ROS2 autonomy and simulation
Mid-Level Full-Stack Engineer specializing in AI/ML and cloud-native web apps
Mid-level Machine Learning Engineer specializing in MLOps and Generative AI
Mid-Level Software Engineer specializing in ML and healthcare data systems
Mid-level Software Development Engineer specializing in Java microservices and FinTech
Mid-level Business Systems Analyst specializing in financial systems and QA
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.”
Vamsidhar Vuddagiri
Mid-level AI/ML Engineer specializing in LLMs, MLOps, and Azure
“AI/ML engineer who led Impacter AI’s production deployment of a specialized outreach LLM (CharmedLLM) fine-tuned on GPT-4.1, cutting API costs ~40% while boosting outreach effectiveness ~60%. Built the supporting MLOps and data infrastructure (MLflow, Kubernetes, PySpark, Kafka) and has agentic AI experience from University of Dayton, using LangChain + RAG and vector search (Pinecone) to improve reliability and reduce hallucinations.”
revanth polthi
Mid-level Data Scientist specializing in Generative AI and MLOps
“GenAI/LLM engineer with production experience at Allstate building an end-to-end document intelligence workflow for insurance operations—automating document intake, classification, and risk signal extraction. Emphasizes high-reliability design for regulated/high-stakes outputs using schema enforcement, confidence thresholds, validation rules, and human-in-the-loop routing, with metric-driven offline evaluation and production monitoring.”
Mid-level Machine Learning Engineer specializing in healthcare and enterprise analytics