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Vetted Logistic Regression Professionals
Pre-screened and vetted.
Mid-level Full-Stack Developer specializing in React, Node.js, and AI automation
Mid-level Generative AI Engineer specializing in LLMs, RAG, and prompt engineering
Mid-level AI/ML Research Engineer specializing in NLP, LLM agents, and multimodal systems
Junior Full-Stack Software Developer specializing in cloud-native apps and data/AI
Mid-level Full-Stack Developer specializing in Python, React, and cloud-native microservices
Mid-level Machine Learning Engineer specializing in Generative AI and healthcare NLP
Junior Data Scientist/Data Analyst specializing in machine learning and business intelligence
“Built and shipped a production-grade RAG-powered news summarization and Q&A product, tackling real-world issues like retrieval drift, hallucinations, latency, and autoscaling deployment (Docker + FastAPI + Streamlit Cloud). Experienced in end-to-end ML/LLM workflow automation using Airflow, Kubeflow Pipelines, and MLflow, and has demonstrated business impact (40% inference precision improvement) through close collaboration with non-technical stakeholders at Evoastra Ventures.”
richi ibnu wardana
“Built an automated ML/NLP document classification system for unstructured legal documents, combining classical models (TF-IDF + logistic regression/random forest) with entity resolution via fuzzy matching validated by precision/recall. Also implemented semantic similarity search using sentence embeddings stored in FAISS and improved matching by fine-tuning a transformer on domain-specific data and tuning similarity thresholds for fewer false positives.”