Applied AI Engineer - (Remote - US)

As an Applied AI Engineer at Jobgether, your primary responsibilities will include designing and implementing impactful AI and machine learning solutions for enterprise clients. This involves developing AI pipelines, leading customer workshops, and collaborating with engineering teams to enhance the AI platform. You’ll also maintain communication with client stakeholders to ensure alignment and success in project delivery.

To be considered for the Applied AI Engineer position at Jobgether, you should have at least three years of experience in a customer-facing role, proficient coding skills in Python, and familiarity with tools like PyTorch and OpenAI APIs. A Bachelor’s degree in Computer Science or a related field is essential, along with a proven ability to translate business needs into scalable AI models.

The work environment for an Applied AI Engineer at Jobgether is flexible and remote-first, allowing you to work from anywhere in the US. This role thrives in fast-paced settings, promoting a culture of innovation and collaboration while balancing multiple projects. You'll have the support of a dynamic team focused on driving business value through AI.

Yes, the Applied AI Engineer role at Jobgether does require some travel, though it’s limited to about 25% of your time. This travel will primarily involve engaging with clients and conducting workshops to enhance their understanding of AI adoption and best practices.

Jobgether offers a competitive benefits package for the Applied AI Engineer role, including a salary range of $172,000 to $240,000, equity options, health, dental, and vision coverage, a 401(k) retirement plan, generous parental leave, wellness stipends, and a supportive remote work culture that encourages professional growth.

In your answer, share specific examples of AI and machine learning projects you’ve worked on, detailing your role and the impact of the solutions you developed. Highlight tools and techniques you used, such as Python, PyTorch, or scikit-learn, and how you collaborated with teams to meet business goals.

Discuss your approach to soliciting and integrating client feedback into your AI projects. You might mention the importance of maintaining open communication with stakeholders and how this feedback has led to successful project outcomes or improvements in AI deployments.

Talk about your methods for conducting workshops and training sessions. Explain how you tailor your approach to meet the clients' varying knowledge levels and how you ensure they feel supported during the adoption phase of the AI solutions.

Share a specific challenge you faced during an AI project, the steps you took to address it, and the final outcome. Your answer should emphasize your problem-solving skills and how you collaborated with other team members to turn a challenge into a success.

Illustrate your methodology for understanding business needs and how you conduct requirements gathering sessions. Discuss how you translate these needs into technical specifications and collaborate with teams to build effective AI models.

Indicate your preferred tools and frameworks, such as TensorFlow, PyTorch, or Hugging Face, discussing their advantages in terms of usability and effectiveness. Explain how these tools have helped you in your past projects.

Share specific resources like journals, conferences, or online platforms you use to stay informed. Highlight any key trends or technologies you have recently adopted into your work.

Discuss the importance of effectively communicating complex AI concepts to diverse audiences, including non-technical stakeholders. Provide examples of how you’ve succeeded in making AI relatable through presentations or workshops.

If applicable, outline your familiarity with cloud platforms such as AWS, GCP, or Azure, and provide examples of projects where you've deployed AI solutions in these environments. Discuss how cloud services have enhanced your work.

Emphasize the importance of regular check-ins with stakeholders to ensure project goals are aligned with business needs. Give examples of how you've adapted AI solutions based on changing business landscapes or objectives.