Software Engineer, Applied AI

As a Software Engineer, Applied AI at Greenlite, your main responsibilities include building core agent frameworks that enhance LLM usage for large enterprises, creating user-friendly AI experiences, collaborating with customers to solve problems, and leading product development from concept to execution. You'll also be involved in scaling AI services and participating in team growth initiatives.

To qualify for the Software Engineer, Applied AI position at Greenlite, candidates should have experience in building B2B software products, preferably within fintech or enterprise settings. Essential qualifications include strong communication skills, an entrepreneurial spirit, and a customer-centric mindset that prioritizes understanding and addressing customer pain points.

During your first few weeks as a Software Engineer, Applied AI at Greenlite, you'll be immersed in building infrastructure for LLMs, leading customer calls, and developing new features. You'll have opportunities to collaborate closely with the founding team to ensure alignment and contribute to the product's evolution.

As a Software Engineer, Applied AI at Greenlite, you'll work primarily with NextJS, Node.js, Typescript, and Postgres. Familiarity with these technologies will be vital in efficiently building AI solutions and managing data effectively within the team.

An ideal candidate for the Software Engineer, Applied AI role at Greenlite is someone who exhibits customer curiosity, has experience working in fast-paced environments, possesses an entrepreneurial mindset, and is an excellent communicator. These traits will empower you to thrive in the fast-evolving landscape of an early-stage startup.

When answering this question, detail specific projects or applications where you've successfully utilized language learning models. Highlight any challenges you faced and how you overcame them, emphasizing your understanding of LLM capabilities and their real-world applications.

Discuss your systematic approach to troubleshooting, perhaps using an example from your past experience. Highlight how you identify the problem, research potential solutions, and collaborate with team members for effective resolution.

Explain how user feedback is crucial in shaping product features and improving user experience. Share examples of how you've successfully integrated feedback into your projects, leading to enhancements and customer satisfaction.

Share your strategies for prioritizing tasks, illustrating how you balance urgency and importance. Highlight your ability to adapt and stay organized despite changing demands, possibly mentioning tools or techniques that assist you.

Discuss your familiarity with agile methodologies like Scrum or Kanban. Provide examples of how you've contributed to iterative development processes and team sprints to effectively deliver software features.

Mention specific resources such as blogs, podcasts, or communities you follow to stay informed. Encourage continuous learning and show your enthusiasm for keeping abreast of emerging technologies and trends in AI and software engineering.

Discuss specific challenges like navigating ambiguity or rapid changes in processes. Share how you've adapted to such scenarios and contributed to cultivating a positive team culture despite these challenges.

Emphasize your communication skills and ability to translate complex technical concepts into clear language. Discuss strategies you use to ensure that all team members, regardless of their background, can contribute effectively to the project.

Share specific examples of customer problems you've tackled and how you approached finding solutions. Highlight your empathy for customer challenges and any feedback you received after implementing successful solutions.

Discuss the metrics and KPIs you utilize to assess the effectiveness of your AI solutions. Mention how you analyze feedback and data to make informed adjustments and continuously enhance AI performance.