Senior AI/ML Engineer

As a Senior AI/ML Engineer at Workato, you will be responsible for designing and implementing advanced AI and machine learning systems. This includes developing solutions involving LLMs and embedding technologies, optimizing information retrieval, and integrating AI systems into existing products. You will also collaborate with data scientists to bring new AI models into production while ensuring high-performance scalability.

To apply for the Senior AI/ML Engineer role at Workato, you should possess a Bachelor's degree in Computer Science or a related field along with at least 5 years of experience in backend software development. Strong knowledge in programming languages, particularly Python, and expertise in AI/ML systems, including LLMs and vector databases, is essential.

Workato prides itself on a culture that fosters flexibility, trust, and innovation. As a Senior AI/ML Engineer, you will enjoy a collaborative environment that encourages taking ownership of your projects while maintaining a balance between productivity and self-care. Workato values the input of every employee, making it a vibrant place to grow and innovate.

Essential technologies for the Senior AI/ML Engineer role at Workato include modern programming languages like Python, experience with LLMs, and in-depth knowledge of vector databases and embedding models. Familiarity with AI observability tools, containerization, and cloud platforms like AWS and Azure is also critical for success in this position.

Workato supports the growth of its Senior AI/ML Engineers through continuous learning opportunities, collaboration on innovative projects, and a culture that encourages experimentation and creativity. Employees are empowered to take ownership of their roles and are provided ample resources to stay ahead in the fast-changing AI landscape.

When answering this question, detail your hands-on experience with LLMs, including any specific projects you've worked on involving prompt engineering or fine-tuning methodologies. Highlight key outcomes and how those solutions improved user experiences.

Discuss various optimization techniques such as quantization, distillation, and any other relevant strategies you have employed. Mention specific challenges you've tackled and how these methods contributed to improved performance or efficiency.

Focus on your strategies for maintaining production reliability, including thorough testing, continuous monitoring, and utilizing AI observability tools. Explain how you proactively address model drift and performance issues.

Elaborate on your experience with integration projects, discussing your approach to collaboration with different teams and how you manage integration challenges. Use specific examples to illustrate successful outcomes.

Detail your familiarity with various information retrieval techniques, such as BM25 and TF-IDF, and how you have applied modern neural search approaches in your projects, explaining their impacts on search performance.

Provide a specific example that outlines the problem, your approach to tackling it, and the ultimate results. Emphasize your problem-solving skills and ability to think critically under pressure.

Discuss your coding standards, including practices like code reviews, version control, and continuous integration. Highlight how these practices contribute to maintaining code quality and team collaboration.

Mention the resources you utilize, such as research papers, industry blogs, webinars, or conferences. Discuss how this knowledge informs your work and drives innovation in your projects.

Talk about your hands-on experience with cloud services like AWS Bedrock, Azure OpenAI, or equivalent platforms. Explain how you have used these services in your projects and their impact on scalability and efficiency.

Articulate the importance of cross-functional collaboration, especially when working with data scientists and product teams. Share examples of successful collaborative projects and how they led to better outcomes.