Latin America-Based Senior AI Engineer, Clinical LLMs

WhyHireWrong?; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

WhyHireWrong? is seeking a hands-on AI engineer to advance production clinical AI agents within healthcare workflows. You will design clinical ontologies, fine-tune models, and build robust evaluation suites to catch edge cases that standard metrics miss.

You will work as an individual contributor, focusing on end-to-end model engineering, RAG-enabled retrieval, vector stores, and knowledge graphs to boost agent performance in real-world clinical scenarios.

Qualifications

• Based in Latin America and eligible to work in the region.
• Experience shipping and maintaining AI agents or LLM-based systems in production.
• Hands-on experience training, fine-tuning or distilling models end to end.
• Strong Python programming skills.
• Experience with context engineering (RAG, vector stores and knowledge graphs).
• A track record of shipping production systems; a background limited to academic or research work does not meet this requirement.
• Not currently in a management, director or VP role.

Responsibilities

• Design and maintain clinical ontologies used by production AI agents
• Train, fine-tune and distill language models end to end for clinical use cases
• Build and maintain evaluation frameworks that go beyond standard metrics to identify failure modes
• Ship and maintain AI agents and LLM based systems in production
• Apply context engineering methods, including RAG, vector stores and knowledge graphs, to improve agent performance

Skills

Python programming

Fine-tuning models

End-to-end model training

Context engineering

RAG

Knowledge graphs

Vector stores

AI production systems

Job description

WhyHireWrong? is seeking a hands-on AI engineer to advance production clinical AI agents within healthcare workflows. You will design clinical ontologies, fine-tune models, and build robust evaluation suites to catch edge cases that standard metrics miss.

You will work as an individual contributor, focusing on end-to-end model engineering, RAG-enabled retrieval, vector stores, and knowledge graphs to boost agent performance in real-world clinical scenarios.