AI Training Engineer || Hybrid Onsite | Chicago, IL

Role: AI Training Engineer

Location: Chicago, IL Hybrid Onsite

Contract: Long Term

Role Overview

We are seeking an AI Training Engineer with hands-on experience using Claude Code to design, train, evaluate, and optimize large language models (LLMs). Seeking an AI Engineer Trainer to train the development staff. You will work closely with product, research, and engineering teams to improve model performance, reliability, and alignment with real-world use cases.

Key Responsibilities

• Design and implement training pipelines for LLMs and AI systems
• Create, curate, and preprocess high-quality datasets for model training and evaluation
• Develop prompts, fine-tuning strategies, and reinforcement learning workflows using Claude Code
• Evaluate model outputs using qualitative and quantitative metrics
• Perform error analysis and iterate on training strategies to improve accuracy and safety
• Collaborate with cross-functional teams to deploy AI solutions into production
• Build tools for data labeling, prompt testing, and model monitoring
• Ensure models meet ethical AI standards, safety guidelines, and regulatory requirements

Required Qualifications

• Strong experience in machine learning, NLP, or AI engineering
• Hands-on experience with Claude Code for prompt engineering, evaluation, or model training workflows
• Strong programming skills in Python
• Experience with ML frameworks such as PyTorch or TensorFlow
• Familiarity with APIs and integrating LLMs into applications
• Solid understanding of NLP concepts and transformer-based architectures

Preferred Qualifications

• Experience with LLM fine-tuning, RLHF, or instruction tuning
• Familiarity with tools like LangChain, vector databases, or retrieval-augmented generation (RAG)
• Experience working with large datasets and distributed training systems
• Knowledge of AI safety, bias mitigation, and alignment techniques
• Contributions to open-source AI or ML projects

Key Skills

• Prompt engineering and evaluation
• Data annotation and curation
• Model performance analysis
• Experiment design and A/B testing
• Problem-solving and critical thinking
• Strong communication and collaboration