AI Architect (Remote - United States)

An AI Architect at Jobgether primarily focuses on designing and deploying scalable AI solutions for healthcare environments. This includes leading the development of machine learning and NLP models from conception to production, defining architectural frameworks, and collaborating across various teams to identify AI use cases. The role requires practical hands-on experience, especially in maintaining compliance with data privacy regulations while ensuring performance and scalability.

To be considered for the AI Architect role at Jobgether, candidates should have a minimum of 5 years of experience building machine learning or AI systems, with at least 3 years in a technical leadership role. Familiarity with NLP and LLM models is essential, ideally applied within healthcare. Moreover, experience with platforms such as OpenAI and orchestration tools like MLflow is advantageous.

As an AI Architect with Jobgether, you'll receive a competitive salary complemented by performance bonuses, comprehensive health benefits, and a flexible paid time off policy. Additionally, you'll have access to a 401(k) retirement plan with employer contributions and ongoing professional development opportunities to upskill in your field.

While specific certifications are not mandatory, having recognized qualifications in data science, machine learning, or AI can enhance your application for the AI Architect role at Jobgether. The emphasis is primarily on hands-on experience and a proven track record in deploying AI solutions, particularly in compliance-heavy environments like healthcare.

Yes, your application for the AI Architect position at Jobgether will undergo an AI-powered three-step screening process. This sophisticated approach evaluates your resume and LinkedIn profile to ensure a match based on qualifications and relevance to the role.

In answering this question, talk about the importance of understanding the business requirements, identifying specific use cases, and ensuring scalability and compliance throughout the design process. Illustrate your approach with specific examples of past projects you’ve tackled.

This question gives you the opportunity to share your expertise in natural language processing and large language model implementation. Be specific about the frameworks you used and examples of how these have been employed in real-world applications, especially within healthcare.

Discuss specific challenges, such as data privacy issues or model deployment hurdles, and elaborate on the strategies you implemented to address these challenges. Reflecting on real-life experiences will demonstrate your problem-solving skills and adaptability.

Compliance in healthcare is paramount. Talk about your knowledge of relevant regulations such as HIPAA, and outline the processes you put in place to maintain compliance throughout the AI development lifecycle, from data acquisition to model deployment.

Here, you should mention the tools such as TensorFlow, PyTorch, and orchestration tools like MLflow. Discuss why these are your tools of choice, citing their capabilities that align with successful project completions.

Share a specific example where an AI project led to measurable business outcomes. Use metrics to illustrate impact and discuss your role in leading that project from conception to implementation.

Talk about your mentoring style and how you nurture talent when guiding junior staff through AI projects. Include examples of success stories where your mentorship has resulted in improved project dynamics.

Explain your approach to continuous learning. Discuss the sources of your knowledge, whether it’s academic journals, online courses, or industry conferences, and how you actively implement these advancements in your work.

Outline your approach to model lifecycle management, including the stages from initial training to deployment, monitoring, and updating models. Illustrate the importance of performance tracking in maintaining effective AI systems.

Discuss the significance of responsible AI development. Elaborate on how you integrate ethical considerations into your AI projects, such as ensuring fairness and reducing bias in machine learning models.