Staff Software Engineer, AI - (Remote - US)

As a Staff Software Engineer, AI at Jobgether, your responsibilities include designing and developing AI-powered enterprise applications, implementing RAG pipelines, and integrating various LLM APIs. You will also mentor junior engineers, optimize AI workflows, and collaborate closely with product managers and designers to enhance user experiences while ensuring application scalability and reliability.

To qualify for the Staff Software Engineer, AI role at Jobgether, you should possess a Bachelor’s degree in Computer Science or a related field, with at least 5 years of professional experience in software engineering and 3 years of applied AI/ML experience. Proven experience integrating commercial LLM APIs and understanding of cloud-native development are essential.

In the Staff Software Engineer, AI position at Jobgether, key skills include proficiency in cloud-native application development, a deep understanding of AI model evaluation, and hands-on experience with RAG-based systems and vector databases. Strong collaboration and communication skills are also crucial for working within cross-functional teams.

At Jobgether, growth opportunities for a Staff Software Engineer, AI are abundant. You will have access to professional development resources, mentorship from senior engineers, and opportunities to lead projects, paving the way for career advancement and skill enhancement in the dynamic field of AI.

Jobgether provides a flexible work environment with remote work options, competitive salaries, health insurance, wellness benefits, floating holidays, and a quarterly wellness day. We prioritize inclusivity and encourage employees to bring their authentic selves to work, creating a collaborative and supportive atmosphere.

When answering this question, highlight your specific experiences with LLM APIs such as OpenAI or Anthropic. Discuss projects where you have successfully integrated these technologies, the challenges faced, and how you overcame them, showcasing your problem-solving skills.

A RAG pipeline stands for Retrieval-Augmented Generation pipeline. It is essential because it allows AI systems to generate more accurate and contextually relevant responses by utilizing retrieved information. Emphasize your understanding of both retrieval and generation processes and how they can improve application performance.

To answer effectively, discuss best practices in building cloud-native applications, such as utilizing serverless architectures, monitoring system performance, and implementing error handling. Give examples from your past experiences where you've ensured application reliability and scalability.

Highlight your mentoring philosophy and provide specific examples of how you have guided junior engineers in their work. Talk about any training you've offered, feedback provided, and the overall impact your mentorship has had on their growth and project outcomes.

Detail specific AI techniques you've leveraged, such as fine-tuning models, prompt engineering, or utilizing vector databases. Discuss the context in which you applied them and the results they yielded, demonstrating your hands-on experience and technical competency.

Discuss your strategies for effective collaboration within cross-functional teams. Emphasize clear communication, regular meetings, and your ability to incorporate diverse perspectives in finding solutions or delivering projects, illustrating your teamwork skills.

When responding, describe your methodology for identifying, tracking, and resolving errors in AI applications. Provide examples of common errors you've encountered and how you addressed them to maintain application performance and reliability.

Choose a specific project that posed significant challenges, describe the obstacles faced, and detail the steps you took to overcome them. Focus on your problem-solving skills, collaboration, and how the project ultimately succeeded due to your efforts.

Detail any innovative ideas or solutions you have proposed or implemented in past roles. Discuss the impact of these innovations on project outcomes, team efficiency, or consumer feedback, reinforcing your creativity and contributions to AI advancements.

Illustrate your experience with cloud-native technologies such as AWS services like Lambda, API Gateway, and DynamoDB. Give examples of projects where you employed these technologies to build scalable and efficient applications, showcasing your technical expertise.