As an Applied AI Engineer at maisa AI, you will be responsible for designing and implementing end-to-end AI agent workflows using our KPU technology. This role involves optimizing agent workflows for performance, accuracy, and explainability while building robust evaluation frameworks to measure agent performance and reliability. You will collaborate with customers to understand their needs and iterate on solutions rapidly, driving innovation in AI agents.

The ideal candidate for the Applied AI Engineer position at maisa AI should have at least 4 years of engineering experience in AI-focused roles, with a solid understanding of LLMs, RAGs, and the challenges associated with enterprise automation. Familiarity with evaluation methodologies for AI systems and hands-on experience in coding with Python and JavaScript/TypeScript are crucial. A self-starter mentality and the ability to thrive in a fast-paced startup environment will also benefit you in this role.

At maisa AI, we prioritize the reliability and accountability of our AI agents by implementing robust evaluation frameworks that measure performance and continuously optimizing the agent workflows. The Applied AI Engineer plays a critical role in developing innovative approaches to enhance our chain of work traceability and explainability, ensuring that our solutions not only perform effectively but are also transparent to the users.

An Applied AI Engineer at maisa AI should be well-versed in LLMs, RAGs, and advanced RAGs, as well as semantic AI. It's essential to have a good grasp of their capabilities and limitations in enterprise applications. Familiarity with prompt engineering and LLM fine-tuning will also be beneficial in creating effective AI-powered features and workflows.

The work environment at maisa AI is dynamic and collaborative, characterized by a flat organizational structure where your impact is valued more than title or hierarchy. You will have the opportunity to work with a talented team of technical experts and engage directly with enterprise customers, addressing their challenges while contributing to meaningful AI innovations.

When discussing your experience with LLMs and RAGs, it's essential to elaborate on specific projects you've worked on, the techniques you used, and the outcomes achieved. Highlight any challenges faced and how you overcame them, as well as your understanding of these technologies' capabilities and limitations.

In answering this question, you could detail specific evaluation methodologies and metrics you've applied in your previous roles. Discuss how these methodologies help measure reliability and performance, and share any experiences where your evaluations led to meaningful improvements in AI agents.

Share your process of engaging with users to understand their needs. Highlight any methods you use to gather feedback and iterate on your solutions. Provide examples of how user feedback has informed your work and led to successful outcomes in AI solutions.

This question gives you the opportunity to showcase your technical skills. Provide a detailed description of the system, your role in its design, and the specific problems it addressed. Quantify the impact if possible—such as performance improvements or increased reliability—to convey the significance of your contributions.

Discuss your approach to integrating explainability in AI design. Mention any frameworks or techniques you use to make AI decisions transparent for users. Highlight any collaboration with stakeholders to ensure they understand the AI's decision-making process and how it impacts their workflows.

Illustrate your ability to adapt and respond to feedback by sharing a specific situation where you quickly modified an AI solution. Discuss the factors that prompted this iteration and the positive outcomes that resulted from your responsiveness to user needs.

Explain your strategies for keeping up-to-date with trends and advancements in AI, such as attending workshops, engaging with the AI research community, or following relevant publications. Highlight how this knowledge has informed your work and contributed to your professional growth.

Be specific about your skills in Python and JavaScript/TypeScript, and provide examples of how you have utilized these languages in projects. Discuss the significance of these languages in your daily work as an Applied AI Engineer and any libraries or frameworks you are familiar with.

Reflect on your time management strategies and tools you use to set priorities and meet deadlines. Share specific examples of how you've successfully balanced multiple tasks or projects while delivering quality results in a high-pressure environment.

Here, share your personal motivations for working in the AI field, emphasizing the importance of building systems that are reliable and responsible. Discuss how your values align with this goal and the impact you hope to have on users and the industry overall.