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Vetted Natural Language Processing Professionals
Pre-screened and vetted.
Senior Machine Learning Engineer specializing in NLP and production ML systems
Mid-level Frontend Engineer specializing in React/Next.js SaaS dashboards
Mid-level Python Developer specializing in cloud-native APIs and microservices
Mid-level Full-Stack AI Engineer specializing in RAG systems and intelligent automation
Mid-level AI/ML Engineer specializing in LLMs and RAG systems
Senior AI/ML Engineer specializing in NLP, LLMs, speech, and computer vision
Senior AI/ML Engineer specializing in NLP, LLMs, speech, and computer vision
Mid-level AI Engineer specializing in LLMs, RAG, and enterprise compliance & fraud systems
Junior Full-Stack Software Engineer specializing in cloud-native microservices and data pipelines
Senior AI/ML Engineer specializing in NLP, LLMs, speech, and computer vision
Mid-level Machine Learning Engineer specializing in Generative AI and healthcare NLP
Junior Data Scientist/Data Analyst specializing in machine learning and business intelligence
Intern Full-Stack Software Engineer specializing in AI-powered applications
Junior Front-End Engineer specializing in React/Next.js and accessible UI
Junior Data Annotator and Computer Science graduate specializing in AI data labeling
Richa Chanpuriya
Mid-level Backend/Agentic AI Engineer specializing in GenAI automation and RAG systems
“Built and shipped a production AI-driven privacy automation system that autonomously navigates data broker sites to submit opt-out/data deletion requests end-to-end, including robust CAPTCHA detection/solving (e.g., reCAPTCHA/hCaptcha/Cloudflare) via 2Captcha. Experienced in orchestrating stateful LLM agent workflows with LangGraph and hardening them for production with strict state management, retries/fallbacks, validation layers, and database-backed observability/audit logs, collaborating closely with legal/compliance stakeholders.”
Manas Kumar Sherla
Junior Machine Learning Engineer specializing in NLP and LLM-based clinical AI
“Built a production automated resume matching system using Python, FAISS vector search, and Selenium-based job scraping, including mitigation for IP blocking and heterogeneous site structures. Also develops LLM/RAG applications with LangChain, using Pydantic-guardrailed structured outputs and LLM-as-a-judge evaluation (including a project focused on tone/semantics for a 3D avatar’s emotional responses).”
Minh Huynh
Junior AI/ML Engineer specializing in LLM systems and personalization
“Backend engineer who built and scaled AmazonProAI, a multi-tenant SaaS platform for Amazon sellers, using a modular Django/DRF monolith with strict seller-level isolation and security controls. Led a controlled SQLite-to-PostgreSQL migration and hardened bulk Excel ingestion with idempotency and data integrity constraints to prevent duplicate metrics and noisy alerts while keeping the system ready for future service extraction.”
“Built a production LLM-powered interview-prep app that ingests job postings and generates tailored preparation plans. Iterated from a single generalist LLM to a multi-LLM pipeline and used RAG to ground the final chat assistant on locally stored intermediate outputs; has also experimented with n8n vs Python-coded pipelines for orchestration.”
Mohammad Al-Hudban
Intern AI Engineer & Data Scientist specializing in GenAI, LLMs, and RAG
“Currently working at CBS Lab in Austria, where they implemented/replicated the "Open World Grasping" research pipeline end-to-end. Built a ROS-based RGB-D perception-to-action system using SAM 2.1 segmentation and MoveIt motion planning to generate grasp poses and execute pick-and-place/sorting with a robotic arm.”
Junior Machine Learning Engineer specializing in Agentic RAG and Document AI
“Built and shipped a production-grade RAG-powered news summarization and Q&A product, tackling real-world issues like retrieval drift, hallucinations, latency, and autoscaling deployment (Docker + FastAPI + Streamlit Cloud). Experienced in end-to-end ML/LLM workflow automation using Airflow, Kubeflow Pipelines, and MLflow, and has demonstrated business impact (40% inference precision improvement) through close collaboration with non-technical stakeholders at Evoastra Ventures.”