Finance AI Engineer: Deployable, Production-Ready Models

Stealth Startup; California (MO); On-site; USD 120,000 - 180,000; Full time

Job summary

Stealth Startup seeks an Applied AI Engineer to design, train, and deploy production-ready AI models powering next-generation AI agents in financial workflows. You will translate business problems into scalable AI systems in collaboration with product, engineering, and domain experts.

The role focuses on fine-tuning, post-training optimization, and building reliable pipelines using both open-source and proprietary data, with emphasis on real-world production deployments and robust evaluation.

Qualifications

• 1–4 years of hands-on experience training or fine-tuning ML/AI models.
• Experience working with LLM or multimodal model training pipelines.
• Strong Python skills and familiarity with deep learning frameworks.

Responsibilities

• Fine-tune large language models and multimodal models for domain-specific use cases.
• Implement and optimize training workflows using open-source frameworks.
• Experiment with model architecture improvements and hyperparameter optimization.
• Build and improve AI agents capable of executing multi-step workflows.
• Integrate models into production environments and product features.
• Improve model reliability, accuracy, and robustness for high-stakes applications.
• Develop evaluation and testing frameworks for model performance and edge cases.
• Work with proprietary domain datasets to improve model specialization.
• Design training datasets, labeling pipelines, and data quality frameworks.
• Conduct ablation studies and performance benchmarking.

Skills

Python

LLM training

Multimodal models

Model deployment

Training pipelines

Hyperparameter optimization

Job description

Stealth Startup seeks an Applied AI Engineer to design, train, and deploy production-ready AI models powering next-generation AI agents in financial workflows. You will translate business problems into scalable AI systems in collaboration with product, engineering, and domain experts.

The role focuses on fine-tuning, post-training optimization, and building reliable pipelines using both open-source and proprietary data, with emphasis on real-world production deployments and robust evaluation.