.png){kind=logo}
Vetted Neural Networks Professionals
Pre-screened and vetted.
Junior Data Analyst specializing in analytics, ETL, and machine learning
Mid-level Machine Learning Engineer specializing in multimodal GenAI and RAG systems
Mid-level Data Scientist specializing in deep learning, NLP, and time-series forecasting
Mid-level Generative AI/ML Engineer specializing in LLMs, RAG, and MLOps
Mid-level Data Scientist specializing in NLP, Generative AI, and ML pipelines
Senior AI/ML Engineer specializing in NLP, computer vision, and cloud ML systems
Senior Generative AI Engineer & Full-Stack Developer specializing in LLMs and microservices
Mid-level Robotics & AI Researcher specializing in learning-augmented task and motion planning
Mid-level Data & Analytics Professional specializing in BI, ML, and SaaS products
Mid-level Software Engineer specializing in algorithms, ML systems, and data infrastructure
Intern Full-Stack Software Engineer specializing in cloud, microservices, and ML/NLP
Mid-level Generative AI Engineer specializing in LLMs, RAG, and NLP systems
Junior Full-Stack Software Engineer specializing in cloud, distributed systems, and AI
Avni Tripathi
Mid-level Data Scientist specializing in NLP, RAG, and information retrieval for RegTech
“Built and deployed a production document Q&A/research platform that combines semantic search (vector DB embeddings) with structured knowledge-graph querying to reduce analyst research time. Used in high-stakes domains like Politically Exposed Person profiling and extracting critical information from ESG/regulatory documents, with a human-in-the-loop evaluation process (precision@k and source-text highlighting) to ensure accuracy.”
Vishesh Patel
Junior AI/ML Engineer specializing in Python ML, NLP, and model deployment
“Built and productionized a real-time social-media sentiment analysis system used by a marketing team to monitor brand/campaign performance. Experienced in orchestrating LLM workflows with LangChain (validation → prompting → parsing → post-processing), plus monitoring, retraining, and RAG-style retrieval using embeddings/vector stores to keep outputs reliable over time.”