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Vetted LoRA Professionals
Pre-screened and vetted.
Mid-Level Full-Stack Software Developer specializing in microservices and AI/LLM solutions
Mid-level Machine Learning Engineer specializing in LLMs and ML at scale
Mid-level AI Engineer specializing in LLM agents, RAG, and knowledge graphs
Senior Data Scientist specializing in LLMs, Agentic AI, and MLOps
Senior FP&A Analyst and Data Scientist specializing in ML, automation, and analytics
Senior Generative AI Engineer specializing in RAG, LLM fine-tuning, and AI agents
Mid-level AI/ML Engineer specializing in LLM fine-tuning, NLP, and MLOps
Senior AI/ML Engineer specializing in healthcare AI, LLMs, and MLOps
Senior Generative AI & Machine Learning Engineer specializing in LLMs and MLOps
Mid-Level Software Engineer specializing in full-stack development and LLM/GenAI systems
Senior Machine Learning Engineer specializing in LLMs, agentic AI, and MLOps
Joseph D’Alessandro
Senior Game & XR Developer specializing in Unity, Unreal Engine, and immersive learning
“UE5/VR-focused developer who has shipped real UE5 deliverables (Fab.com products and VR template projects used by graduate students) and built a modular VR training interaction system end-to-end using Blueprint Interfaces and Actor Components. Also shipped a game-ready UE5 character product (“Ryan the incredible”) with 180 animations and a scalable AnimBP, and has hands-on profiling/optimization experience reducing tick-heavy Blueprint logic.”
Siliang Zhang
Intern Machine Learning Engineer specializing in LLMs, RAG, and vision-language systems
“Robotics ML/software engineer focused on Vision-Language-Action control for 7-DoF robots, replacing tokenized action decoding with continuous regression heads (including a logit-weighted expectation approach) to improve stability and real-time behavior. Strong in ROS1/ROS2 systems integration and debugging closed-loop manipulation issues via latency instrumentation, QoS-aware distributed messaging, and sim-to-real validation using Gazebo/Unity, Docker, and CI pipelines.”
“Built and deployed a production RAG-based LLM Q&A and summarization platform for internal documents, emphasizing grounded answers with structured prompting and citations to reduce hallucinations. Experienced orchestrating end-to-end LLM workflows with LangChain plus cloud pipelines (Azure ML Pipelines, AWS), and runs iterative evaluation using both metrics (accuracy/hallucination/latency/cost) and real user feedback to drive reliability.”