Applied AI Engineer

Role overview Build the AI engine that turns plain-language problem descriptions into runnable quantum code and publication-quality reports. You will shape system architecture and develop multi-agent language model workflows, while collaborating with quantum physicists and contributing to client machine learning projects as needed.

Responsibilities - Design and maintain agentic AI systems using language model APIs, including multi-agent pipelines, tool-calling workflows, and orchestration. - Integrate language models into product features with attention to reliability, scalability, and maintainability. - Make architecture decisions within a small engineering team and own solutions from design through deployment. - Evaluate developments in the language model and agent ecosystem and propose tool or platform changes. - Contribute to client machine learning work such as forecasting, anomaly detection, and classification.

Requirements - At least three years of hands-on AI/ML engineering experience. - Strong Python skills and experience delivering production-grade systems. - Deep understanding of prompt engineering, function calling, retrieval-augmented generation, and context management. - Practical experience designing agentic systems and multi-agent workflows. - Experience with language model provider APIs. - Solid classical machine learning skills and ability to deliver end-to-end solutions.

Nice to have - Interest or experience in quantum computing, including Qiskit or PennyLane. - Experience with quantum machine learning.

Benefits and work setup - Remote-first role with flexible hours and autonomy. - Access to language models and API credits, with opportunities to learn quantum computing alongside researchers. - Equity participation and performance bonuses.