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Vetted Natural Language Processing Professionals
Pre-screened and vetted.
Junior Data Scientist specializing in applied machine learning and analytics
Mid-level Data Scientist specializing in ML, NLP, and production AI workflows
Mid-Level Full-Stack Software Engineer specializing in modern web apps and microservices
Mid-level Data Scientist specializing in ML, deep learning, and manufacturing analytics
Senior Software Engineer specializing in AI agents, RAG, and enterprise search in Financial Services
Senior Data Scientist specializing in marketing analytics, attribution, and revenue forecasting
Mid-level Generative AI Engineer specializing in LLMs, RAG, and agentic AI
Mid-level AI/ML Engineer specializing in NLP, LLMs, and MLOps
Executive Technology Leader specializing in AI, Cloud, and FinTech/EdTech platforms
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
Senior Full-Stack AI/ML Engineer specializing in personalization, NLP, and GenAI platforms
Mid-level Data Scientist / ML Engineer specializing in LLMs and predictive analytics
Senior Data Scientist specializing in healthcare analytics and scalable ML pipelines
Anshul Joshi
Mid-Level Software Engineer specializing in distributed systems and GenAI
“Capgemini engineer with 4+ years building and deploying high-availability, low-latency fraud detection APIs and multi-cluster distributed systems for a Fortune 20 bank, including zero-downtime production rollouts and multi-layer (SQL/network/hardware) performance debugging. Also built a Python + OpenAI/LangChain LLM-powered grading workflow for Austin School for Women, cutting feedback time from 90 minutes to 5 minutes per submission for 200+ learners.”
Pavan Kalyan
Mid-level AI Engineer specializing in GenAI agents and RAG for IT operations
“Built and operates a production LLM agent for enterprise IT operations that triages and drafts resolutions for high-volume ServiceNow tickets using LangChain + RAG (Pinecone/pgvector) and AWS Bedrock/OpenAI. Emphasizes reliability with schema-validated stages, offline eval datasets from real tickets, and CloudWatch-driven monitoring/guardrails; system scales to 40K+ tickets/month and cut resolution time ~28%.”
Mid-level AI/ML Engineer specializing in conversational AI, NLP, and LLM-powered RAG systems
Senior Data Engineer specializing in multi-cloud data platforms and generative AI
Mid-level AI/ML Developer specializing in FinTech fraud detection and GenAI assistants
Mid-level Data Scientist specializing in financial ML, NLP, and MLOps
Mid-level AI/ML Software Engineer specializing in Generative AI and NLP