Vetted NLTK Professionals

“Analytics-focused candidate with experience turning messy operational and educational program data into validated reporting assets, dashboards, and AI-ready datasets. They stand out for pairing SQL/Python data preparation with rigorous validation and lightweight NLP on open-ended feedback, driving reported improvements in reporting efficiency, engagement, and student success outcomes.”

Angel Diones

Screened

Entry-level Software Developer specializing in full-stack and cloud applications

Remote, USA1y exp

“Internship-heavy software engineer with hands-on experience building and debugging production-style systems in agriculture, hospitality, and logistics. Stands out for solving a deployment-breaking Next.js environment variable issue during an AWS Amplify to ECS migration, plus driving backend-centric architectural improvements for performance, security, and maintainability.”

“Full-stack engineer with strong TypeScript/Firebase and MongoDB depth who has built research and student-scale systems end-to-end, including GCP data pipelines and Dockerized microservices. Particularly interesting for stepping beyond a frontend remit to build NLP models with NLTK that are actively used in an academic research project's Phase 2, showing initiative and real downstream impact.”

“FAU-based analytics candidate with hands-on academic project experience across SQL data preparation, Python/NLP sentiment analysis, and predictive modeling. They stand out for turning messy datasets into clean reporting tables, building reproducible analysis workflows, and translating findings into practical recommendations around operations, credit risk, and marketing ROI.”

“Full-stack engineer and Scrum Master who led a major monolith-to-microservices migration, including a micro-frontend Angular architecture using Native Federation and staged integration into the legacy app. Also built a React + TypeScript “Business Risk” product featuring a metadata-driven dynamic dashboard/forms layer backed by Spring Boot + GraphQL, with strong QA practices (unit/integration/E2E via Cypress), CI/CD, and feature toggles.”

“AI/ML practitioner with production experience building a RAG-powered contextual customer support agent, optimizing for low latency using vector databases and smaller LLMs. Also deployed a fraud detection model on Kubernetes with auto-scaling for heavy transactional loads, and improved chatbot accuracy by 15% through metric-driven testing and evaluation. Partners with Marketing on personalization/recommendation initiatives with measurable outcomes tied to customer feedback.”

“AI engineer with hands-on experience shipping production systems across semantic search, RAG/LLM applications, and computer vision. Built a personalized e-commerce search platform with measurable relevance and latency gains, and deployed grounded GenAI chat systems that significantly reduced hallucinations while lowering support burden. Also brings edge-deployment experience in monocular depth estimation and 3D reconstruction, suggesting strong breadth across modern applied AI.”

“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.”