AI Engineer

The AI Engineer at FinQuery is responsible for developing, maintaining, and improving production AI/ML services. This includes using the latest AI technologies to enhance product features, experimenting with LLM prompts and workflows, integrating AI functionalities with other engineering teams, and providing both tactical and strategic insights related to production AI/ML. Additionally, you'll work in a collaborative environment, ensuring that our services deliver exceptional value to users.

To be considered for the AI Engineer role at FinQuery, candidates should have a strong foundation in AI/ML concepts, experience with production AI/ML models, and proficiency in Python. Ideally, candidates will have at least 2 years of experience in the software industry, and familiarity with end-to-end AI/ML processes, LLM models, and AWS AI/ML tools are also desirable.

FinQuery is committed to professional development and offers numerous opportunities for continuous learning. AI Engineers can participate in mentorship programs, receive stipends for employee development, and engage in regular team outings. The environment encourages teamwork and knowledge-sharing among peers, ensuring that everyone stays updated with the latest AI/ML advances.

At FinQuery, AI Engineers work with a range of cutting-edge technologies suitable for developing and maintaining AI/ML services. This includes utilizing tools for large language models, experimenting with different AI frameworks, and applying best practices in production settings. Familiarity with document processing using LLMs and the AWS stack can be beneficial as well.

FinQuery offers a comprehensive benefits package for its AI Engineers, including flexible PTO, a 401(k) plan with employer matching, multiple health benefit options, and a range of perks like catered lunches, a work-from-home setup stipend, and a generous employee development stipend. Additionally, the company supports a healthy work-life balance with options for remote and hybrid work.

When answering this question, detail the specific AI/ML models you have worked on, your role in their development, deployment, and maintenance processes. Highlight any challenges you faced and how you addressed them, emphasizing your problem-solving skills and your ability to work with cross-functional teams.

Explain your methodology for evaluating and iterating on LLM prompts. Discuss how you use data-driven insights to refine these prompts and improve model performance, showcasing your analytical skills and familiarity with experimental design.

Discuss your collaborative approach when working with cross-functional teams, like product managers and UX designers. Highlight specific examples of past projects where you successfully integrated AI/ML features into broader software solutions, emphasizing communication and alignment with team goals.

Detail the monitoring strategies you implement to ensure the performance and reliability of production AI models. Discuss the tools you use and any key performance indicators (KPIs) that you track, emphasizing your commitment to maintaining high standards for production environments.

Describe your approach to continuous learning, whether through courses, conferences, research papers, or networking with other professionals in the AI/ML field. Highlight specific examples of how you have recently applied new knowledge to your work.

Be specific about your programming proficiency, particularly in Python, and provide examples of how you have used it in past AI/ML projects. Discuss your coding practices, such as writing clean, maintainable code, and any frameworks or libraries you’re familiar with.

Use the STAR (Situation, Task, Action, Result) method to structure your response. Describe the context of the project, your specific responsibilities, the challenges you encountered, and how you successfully navigated them to achieve positive outcomes.

Share your experience in developing LLM-powered features, detailing the specific applications you've worked on. Discuss the challenges faced and techniques used to optimize performance, demonstrating your understanding of LLM capabilities and limitations.

Emphasize your openness to feedback and continuous improvement. Discuss how you actively solicit input from peers and stakeholders, and provide examples of how you have integrated feedback into your projects to enhance outcomes.

Articulate your interest in FinQuery's mission and products, the innovative aspects of the AI Engineer role, and how your skills align with the company’s goals. Express your enthusiasm for contributing to a team that values learning, collaboration, and cutting-edge technology.