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Vetted Matplotlib Professionals
Pre-screened and vetted.
Mid-level Data Analyst specializing in predictive analytics and BI for financial services
Mid-level Data Scientist specializing in ML, deep learning, and manufacturing analytics
Mid-Level Full-Stack Software Engineer specializing in cloud-native and AI-driven applications
Mid-level Generative AI Engineer specializing in LLMs, RAG, and agentic AI
Mid-level AI/ML Engineer specializing in NLP, LLMs, and MLOps
Mid-Level Software Engineer specializing in full-stack, data engineering, and ML
Senior Data Scientist specializing in ML, fraud risk, and Generative AI (RAG/LLMs)
Mid-level AI/ML Engineer specializing in GenAI, computer vision, and real-time ML pipelines
Junior Research Data Scientist specializing in healthcare analytics and real-world evidence
Mid-level Data Scientist / ML Engineer specializing in LLMs and predictive analytics
Senior Data Engineer specializing in multi-cloud data platforms and real-time analytics
Senior Full-Stack Developer specializing in cloud-native microservices (AWS)
Senior Data Scientist specializing in healthcare analytics and scalable ML pipelines
Mid-level AI/ML Engineer specializing in conversational AI, NLP, and LLM-powered RAG systems
Senior Machine Learning Engineer specializing in MLOps and Generative AI
Mid-level Data Scientist specializing in financial ML, NLP, and MLOps
Jayeetra Bhattacharjee
Mid-level AI/ML Engineer specializing in LLMs, NLP, and analytics automation
“AI/ML Engineer (TCS) who built and deployed a production LLM-powered audit transaction validation service to reduce manual review of unstructured transaction records and comments. Implemented a LangChain/Python pipeline for extraction/normalization and discrepancy detection, with strong production reliability practices (decision logging, dashboards, labeled eval sets) and a human-in-the-loop auditor feedback loop to improve precision/recall under strict data-sensitivity and near-real-time constraints.”
Joseph Lin
Intern Software Engineer specializing in full-stack development and applied AI
“Internship experience building an end-to-end medical AI pipeline that extracts and normalizes messy medical PDFs, fine-tunes BioBERT to classify tumor-related statements (including negation/ambiguity handling), and integrates image-model outputs (MedSAM/GroundingDINO) for tumor localization and classification. Also worked on an LLM/RAG system to draft IPO prospectuses using retrieved regulatory/financial sources (including SEC EDGAR) with structured prompts to reduce hallucinations.”