Pre-screened and vetted.
Mid-level Robotics & Computer Vision Engineer specializing in SLAM and edge AI
“Robotics/SLAM-focused engineer who worked on RT-Appearance mapping using NetVLAD, replacing traditional CV feature extraction with a deep learning approach to improve loop closure in repetitive green environments. Has hands-on ROS1/ROS2 experience (including bridging), point-cloud alignment with G-ICP for sensor-parameter matching, and Gazebo+Docker simulation testing for motion planning/perception.”
Senior Software Engineer specializing in full-stack systems, big data, and applied AI
“Built and deployed ForensicLLM, a local domain-specific LLaMA-3.1-8B model for digital forensic investigators using RAFT + RAG over 1000+ curated research papers, with citation-aware responses and rigorous evaluation (BERTScore/G-Eval). Deployed via vLLM and Docker and validated through a chatbot survey with 80+ participants; published at DFRWS EU 2025.”
Junior AI Engineer specializing in LLM agents, RAG, and MLOps
Junior Systems & Backend Software Engineer specializing in cloud infrastructure and healthcare data
Mid-level AI Engineer specializing in Generative AI agents and LLM production systems
Mid-level Generative AI Engineer specializing in agentic LLM systems and RAG
Mid-level Machine Learning Engineer specializing in NLP, Computer Vision, and LLMs
Mid-level Full-Stack Developer specializing in scalable web apps and microservices
Mid-level AI/ML Data Engineer specializing in MLOps and Generative AI
Mid-level AI & Data Engineer specializing in cloud ML, RAG systems, and ETL automation
Mid-level AI Engineer & Data Scientist specializing in LLM agents and RAG systems
Mid-level Full-Stack Developer specializing in real-time systems, AI features, and payments
Senior AI/ML Engineer specializing in Generative AI, RAG, and multimodal LLM systems
Junior AI Engineer specializing in NLP, LLMs, and recommender systems
Mid-level Machine Learning Engineer specializing in Generative AI and LLMOps
Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable model deployment