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Vetted Transformers Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in fraud detection, credit risk, and NLP
Junior Machine Learning Engineer specializing in Generative AI and LLM agents
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
Mid-level Generative AI & ML Engineer specializing in LLMs, RAG, and MLOps
Md Rifat Ullah
Mid-level AI/ML Engineer specializing in NLP, GenAI, and conversational AI
“Built and deployed a production bilingual (Bengali/English) AI virtual assistant that replaced IVR for telecom customer service at massive scale (~15M users), integrating ASR/TTS, Rasa dialogue management, and custom NLP. Overcame low-resource Bengali data and noisy call-center audio with synthetic data augmentation and transformer fine-tuning, achieving significant production gains including ~50% reduction in support calls.”
Junior Full-Stack Software Developer specializing in cloud-native apps and data/AI
Junior Machine Learning Engineer specializing in LLMs, RAG, and fine-tuning
Mid-level AI Engineer specializing in LLMs, RAG, and enterprise compliance & fraud systems
Junior Computer Vision Engineer specializing in generative AI and autonomous perception
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.”