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Vetted Random Forest Professionals
Pre-screened and vetted.
Mid-level Data Scientist specializing in GenAI, NLP, and recommendation systems
Junior Data Scientist specializing in applied machine learning and analytics
Mid-level Data Scientist specializing in ML, deep learning, and manufacturing analytics
Senior Data Scientist specializing in marketing analytics, attribution, and revenue forecasting
Mid-level AI/ML Engineer specializing in NLP, LLMs, and MLOps
Staff-level AI/ML Engineer specializing in enterprise RAG, agentic automation, and AI governance
Senior Data Scientist specializing in ML, fraud risk, and Generative AI (RAG/LLMs)
Junior Research Data Scientist specializing in healthcare analytics and real-world evidence
Mid-level AI/ML Engineer specializing in GenAI, computer vision, and real-time ML pipelines
Mid-level Data Engineer specializing in cloud lakehouse and streaming platforms
Junior Software Engineer specializing in cloud infrastructure and Kubernetes
Mid-level AI/ML Engineer specializing in Generative AI and fraud detection
Mid-level Data Scientist / ML Engineer specializing in LLMs and predictive analytics
Mid-level AI/ML Engineer specializing in conversational AI, NLP, and LLM-powered RAG systems
Mid-level AI/ML Developer specializing in FinTech fraud detection and GenAI assistants
Mid-level Data Scientist specializing in financial ML, NLP, and MLOps
Krishnapriyanka Ponnaganti
Mid-level AI/ML Engineer specializing in agentic AI and production ML systems
“ML/AI engineer with hands-on experience shipping production computer vision and GenAI systems, including a fabric defect detection platform that combined vision models with agentic LLM workflows to reach 89% human-inspector agreement at 200 ms latency. Also built a RAG-based code QA tool for developers and emphasizes production monitoring, evaluation, caching, and reusable Python service design.”
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.”
Soham Patel
Mid-level Machine Learning Engineer specializing in healthcare NLP and MLOps
“ML/AI practitioner in healthcare (Syneos Health) who has deployed production clinical NLP and risk models. Built a BERT-based physician-note information extraction system on Docker + AWS SageMaker (reported ~42% retrieval improvement) and automated retraining/deployment with Airflow and drift detection, while partnering closely with clinicians to drive adoption (reported ~18% readmission reduction).”
Anurag Reddy
Mid-level Data Scientist specializing in ML, MLOps, and Generative AI
“ML/NLP engineer who built a RAG-based technical assistant for Caterpillar field engineers, transforming PDF keyword search into intent-based semantic retrieval across manuals, logs, sensor reports, and technician notes. Strong in productionizing data/ML systems (Airflow, PySpark) with rigorous preprocessing, entity resolution, and evaluation—delivering measurable gains in accuracy, relevance, and duplicate reduction.”