Lead AI Engineer (LLMs)

TL;DR

Lead AI Engineer (LLMs): Building and deploying intelligent systems that extract and structure data from unstructured documents at scale, with an accent on language models (SLM/LLMs) and document understanding. Focus on designing ML/NLP pipelines (OCR, NER, classification, summarization) and integrating LLMs with retrieval (RAG), vector databases, and structured outputs while mentoring engineers and ensuring production quality.

Location: Remote (US)

Salary: $216,417.00 - $324,625.00 (base)

Company

hirify.global is an AI-first healthcare technology company focused on improving pre-payment accuracy and reducing waste in the healthcare ecosystem.

What you will do

• Lead architecture, development, and deployment of AI/ML systems for document ingestion, understanding, and data extraction.
• Create high-quality datasets and implement validation, verification, and quality checks for ML systems.
• Build and fine-tune LLMs/generative AI models to interpret, summarize, and extract information from complex unstructured content.
• Develop NLP pipelines using OCR, entity recognition, text classification, summarization, and semantic parsing.
• Integrate LLMs with retrieval systems (RAG), vector databases, and structured outputs for downstream consumption.
• Mentor AI/ML engineers and establish best practices for training, evaluation, and monitoring in production.

Requirements

• Minimum 7 years of AI/ML engineering experience, including at least 3 years in a technical or team leadership role.
• Hands-on experience building and deploying S/LLMs or generative AI applications.
• Proven experience extracting structured data from unstructured sources (scanned forms, free-text reports, complex layouts).
• Strong Python and ML framework skills (e.g., PyTorch; experience with MLOps tooling such as Kubeflow and CI/CD).
• Experience with OCR and NLP methods (embeddings, transformers, NER, text classification) and production model deployment.
• Familiarity with cloud platforms (AWS, GCP, or Azure) and MLOps practices (version control, CI/CD, monitoring).

Culture & Benefits

• Remote work with a US location requirement.
• Work on AI systems applied to regulated, real-world document processing challenges in healthcare.
• Opportunity to lead and mentor a team of AI/ML engineers and shape best practices for model quality and monitoring.
• Base salary range provided for the full-time US role.

Hiring process

• Interviews focused on AI/ML leadership, document understanding/data extraction experience, and production deployment practices.
• Technical evaluation of LLM/NLP pipeline design, RAG/vector database integration, and validation/monitoring approach.