AI Engineer (FSI)

As an AI Engineer at our company, you will be charged with the end-to-end implementation of generative AI and LLM-based solutions tailored for financial services clients. This includes designing and developing AI agents focused on enhancing customer support within banking, insurance, and asset management. Expect to create intelligent call center solutions that effectively merge voice technology with advanced language models, while also working on document processing workflows for financial documents.

To be successful as an AI Engineer in the FSI industry with our company, you should have at least 3 years of professional experience in banks, insurance companies, or financial technology service providers. A Bachelor's degree in Mathematics, Computer Science, Statistics, or a related field is essential. Additionally, expertise in AWS AI/ML services and hands-on experience with Generative AI solutions are crucial for this role.

For the AI Engineer role at our company, strong technical skills are essential. You should be familiar with the AWS AI/ML stack, including Bedrock, SageMaker, and Comprehend. Experience in using LLM frameworks and voice conversational AI solutions is also important. Beyond that, debugging machine learning models and creating comprehensive documentation are key components of this position.

Our AI Engineer plays a pivotal role in maintaining an exceptional customer satisfaction index (CSI) at 100%. By developing intelligent AI agents and optimizing workflows, you'll create solutions that enhance customer interactions and streamline processes, thereby ensuring that our clients receive the highest quality of service and value from our technological innovations.

As an AI Engineer in our team focusing on the financial services and insurance industry, you will engage in various projects including customer support optimization, building chatbots and voice solutions for call centers, and implementing document processing automations. Each project is designed to improve efficiency and enhance the user experience in financial interactions, ensuring that every client gets the most out of our innovative AI solutions.

When answering this question, highlight specific projects where you developed AI solutions for financial contexts. Discuss the implementation process, technologies used, and the impact of your work on customer operations. Be sure to convey what you learned through these experiences and how they prepared you for this AI Engineer role.

To effectively answer this, outline your experience with AWS AI/ML services like SageMaker and Bedrock. Describe projects that utilized these services and explain how they contributed to successful outcomes. This shows your practical knowledge and ability to leverage AWS tools in your work.

In responding to this question, detail your debugging process, including how you use AWS CloudWatch and SageMaker debugging tools. Provide examples of challenges you've faced and how you resolved them, emphasizing your analytical skills and perseverance.

Discuss your understanding of conversational AI's role in improving customer interactions, particularly in the financial sector. You could mention specific applications like chatbots and AI agents that you’ve worked on, and highlight the benefits such as increased efficiency and personalized service.

Offer a concise overview of a project where you designed and implemented document processing workflows, especially if it involved multimodal LLMs. Focus on the goal of the project, the challenges faced, and the technologies applied to achieve successful results.

Explain how you prioritize understanding customer needs and how that informs your AI development process. Discuss how effective communication during your projects and seeking feedback can help enhance overall customer satisfaction, aligning with your role as an AI Engineer.

Detail your understanding of emotional intelligence in AI and discuss strategies you've implemented or would implement to make AI interactions more human-like. This could include using sentiment analysis or training models to recognize and respond to emotional cues.

Share instances where you have worked with voice synthesis and its applications within customer service. Explain any specific technologies you utilized and how they contributed to enhancing user experiences.

Discuss resources you follow, such as blogs, conferences, or industry publications. Mention any communities or forums you engage with to stay informed about evolving technologies in AI and their applications in the financial realm.

Provide specific examples of challenges you've encountered in optimizing AI for call centers, such as integrating voice technologies or improving response accuracy. Discuss the steps taken to overcome these challenges and the results achieved.