.png){kind=logo}
Vetted Predictive Modeling Professionals
Pre-screened and vetted.
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
Senior QA Engineer specializing in test automation, API testing, and AI-assisted QA
Mid-level Data Scientist specializing in machine learning and analytics
Mid-level Machine Learning Engineer specializing in MLOps and Generative AI
Mid-level Full-Stack Developer specializing in data platforms and analytics dashboards
Mid-level Data Analyst/Data Engineer specializing in machine learning and NLP
Mid-level Data Science & AI/ML Engineer specializing in MLOps, NLP, and computer vision
Senior Data Analyst specializing in healthcare, insurance, and financial analytics
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered analytics
Executive CIO/CTO specializing in AI transformation for healthcare and enterprise operations
Mid-level Backend Python Developer specializing in APIs, ETL, and transaction processing
Junior Business Analyst specializing in real estate finance and data analytics
Mid-level Business Analyst specializing in BI, predictive analytics, and operations
Mid-level AI Software Engineer specializing in healthcare and agentic systems
Shamala Chandrappa
Mid-level AI Engineer specializing in agentic AI, LLM systems, and healthcare AI
“Healthcare-focused ML/AI engineer who has built production voice agents and clinical question-answering systems end-to-end, from experimentation through deployment, observability, and iteration. Particularly strong in making LLM systems reliable in real workflows via RAG, fine-tuning, guardrails, evaluation pipelines, and shared Python tooling; cites ~20% clinical QA accuracy gains and ~40% faster physician decision turnaround.”
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.”
Mid-level Data Scientist & AI Engineer specializing in NLP, computer vision, and MLOps
Mid-level Data Engineer specializing in AI/ML, streaming, and lakehouse architectures
Mid-level Software Engineer specializing in FinTech and scalable backend systems
Mid-level AI/ML Engineer specializing in financial risk, NLP, and MLOps