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Vetted Natural Language Processing Professionals
Pre-screened and vetted.
Mid-level AI Engineer specializing in LLM agents, RAG, and evaluation
Mid-level Generative AI Engineer specializing in LLMs, RAG, and prompt engineering
Mid-level Java Full-Stack Developer specializing in cloud microservices and AI/ML integration
Mid-level AI/ML Engineer specializing in fraud detection, credit risk, and NLP
Mid-Level Software Engineer specializing in SaaS, mobile apps, and trading automation
Senior Data Scientist / ML Engineer specializing in NLP, speech AI, and computer vision
Junior Machine Learning Engineer specializing in Generative AI and LLM agents
Mid-level Generative AI Engineer specializing in LLMs, RAG, and agentic systems
Mid-level AI & Data Science professional specializing in MLOps, deep learning, and UAV research
Junior Full-Stack Developer specializing in AI integrations and LLM research
Mid-Level Full-Stack Software Engineer specializing in cloud microservices and Voice AI
Mid-level AI/ML Research Engineer specializing in NLP, LLM agents, and multimodal systems
Mid-level AI/ML Engineer specializing in GenAI, agentic AI, and RAG pipelines
Intern Full-Stack Developer specializing in React, Node.js, and TypeScript
Mid-level DevOps & Customer Success Engineer specializing in cloud, networking, and GenAI
Haeshitha Indukuri
Entry AI/ML Engineer specializing in Generative AI, LLMs, and MLOps
“Built and productionized a MediCloud/Medicoud LLM microservice platform that lets clinicians query medical data in natural language, orchestrating multi-step RAG-style workflows with LangChain and evaluating/debugging with LangSmith. Delivered measurable gains (consistency ~70%→90% / +20%; latency ~2.0s→1.1s / -40%) by implementing structured prompts, fallback logic across multiple LLMs, hybrid retrieval tuning, and AWS Lambda performance optimizations (package size, async, caching).”
Alishba Ali
Senior Full-Stack Software Developer specializing in React/Node and cloud-native apps
“Frontend engineer who launched Emoot (app.emoot.io) end-to-end and recently shipped the "My Savings Goal" feature from UI through production. Emphasizes scalable React + TypeScript architecture (Storybook-driven component library, strict typing, performance profiling/memoization) and pragmatic delivery in tight timelines by reusing existing components/logic and iterating screen-by-screen.”
Harshit Vashisth
Junior AI Engineer specializing in LLMs, RAG systems, and MLOps
“Robotics software engineer who built an end-to-end system ("justmatrix"), focusing on multi-agent orchestration and a multi-RAG retrieval backend/API. Has hands-on ROS experience, including a custom node for reliable high-frequency sensor data routing, plus deployment automation using Docker, Kubernetes, and CI/CD.”
Junior AI/ML Researcher specializing in deep learning, computer vision, and LLM applications
Junior Software/AI Engineer specializing in LLM agents and RAG systems
Jaiden Kettleson
Entry-Level Full-Stack & AI Engineer specializing in chatbots and web apps
“Data Science honors graduate (Maryville University) who has built Python/SQL backends and a capstone website handling sensitive user data. Emphasizes secure data handling (password encryption, secure database updates) and uses Git/GitHub Pages with CI/CD-style practices for managing and deploying changes.”
Muhammad Murtaza Murtaza
Mid-level Machine Learning Engineer specializing in NLP, Computer Vision & Predictive Analytics
“Built a production LLM fine-tuning pipeline for domain-specific code generation at Pigeonbyte Technologies, including automated collection and rigorous quality filtering of 10M+ code samples (AST validation, sandbox execution/testing, deduplication, drift monitoring, and human-in-the-loop review). Also implemented end-to-end ML orchestration in Apache Airflow with data quality gates, dataset versioning in S3, benchmarking, and automated model promotion, and has a reliability-first approach to agent/workflow design.”