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# AI Engineer I

- **Company:** Billiontoone, Inc.
- **Location:** Menlo Park, United States
- **Experience:** Expert
- **Salary:** $174,241.0 - $200,641.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Automated Storage and Retrieval Systems, Relational Databases, Python (Programming Language), Machine Learning, Tensorflow, Software Engineering, SQL Databases, Workflow Management Systems, Pytorch, Large Language Models, Multi-Agent Systems, Prompt Engineering, Software Application Programming, Build Management, Scikit Learn, Data Pipelines
- **Published:** June 24, 2026
- **Apply:** https://www.dice.com/job-detail/c395a1eb-0dd0-49ea-9d0e-1e0183b79100

## About the Role

* 5+ years of experience in software engineering, data science, or ML engineering
 * Hands-on experience building applications with LLM APIs (OpenAI, Anthropic, etc.)
 * Strong Python proficiency and comfort with SQL and relational databases
 * Experience deploying and maintaining production systems
 * Familiarity with AWS
 * Excellent communication skills and ability to translate business problems into technical solutions
 * Self-directed and comfortable with ambiguity in a 0-to-1 environment

Nice-To-Haves:

 * Previous entrepreneurial experience
 * Experience with retrieval architectures, vector databases, and embedding models
 * Familiarity with agent frameworks or workflow orchestration tools
 * Experience with traditional ML frameworks (scikit-learn, PyTorch, TensorFlow)
 * Prior work in healthcare, biotech, or other regulated industries

## Description

* Build and deploy LLM-powered applications (retrieval systems, AI agents, workflow automation, vector storage) that integrate with internal systems and data sources
 * Partner with business stakeholders to identify automation opportunities, scope requirements, and define success metrics
 * Build lightweight data pipelines and analytics to support AI applications and measure their impact
 * Own projects end-to-end: from prototyping and iteration through production deployment and monitoring
 * Establish best practices for prompt engineering, evaluation, and responsible AI use as the team scales
 * Develop and lead internal training sessions on LLM fundamentals, prompt engineering, and AI-assisted development tools


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