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Vetted Random Forest Professionals
Pre-screened and vetted.
Priyanka Palnati
Entry-Level Computer Science Graduate specializing in ML, data analytics, and cybersecurity
“Built a Smart Resume Screening tool with a React frontend and a Python backend, owning most backend architecture and delivery. Implemented FastAPI endpoints for file upload and NLP/ML inference, created the end-to-end resume classification pipeline, logged predictions to a database for accuracy tracking, and deployed a Dockerized service optimized for low-latency, concurrent processing.”
Entry-Level Technology Consultant specializing in enterprise automation and CRM solutions
Junior Full-Stack Software Developer specializing in cloud-native apps and data/AI
Entry-level Data Analyst specializing in cloud analytics and machine learning
Senior Machine Learning Engineer specializing in NLP and production ML systems
Entry-Level UX Designer and Computer Science Graduate specializing in data-driven product design
Mid-level Full-Stack AI Engineer specializing in RAG systems and intelligent automation
Mid-level AI Engineer specializing in LLMs, RAG, and enterprise compliance & fraud systems
Mid-level Full-Stack Developer specializing in Python, React, and cloud-native microservices
Junior Full-Stack Developer specializing in React, Node.js, and AI/ML
Entry AI Engineer specializing in machine learning, computer vision, and data mining
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.”
Intern Machine Learning Engineer specializing in NLP, RAG, and time-series forecasting