AI Engineer

As an AI Engineer at Capco, your key responsibilities include managing the deployment of our GenAI chat platform, collaborating with technical and business teams to develop innovative GenAI solutions, and evaluating various technologies in the GenAI landscape. You'll engage in agile development, assist in creating prototypes, and ensure that the technical roadmap aligns with business goals.

To qualify for the AI Engineer position at Capco, candidates should have a Bachelor's degree in computer science, engineering, information technology, or artificial intelligence, along with equivalent experience. Proficiency in programming languages such as Python and NodeJS, experience with cloud technologies, and the ability to guide junior resources in design choices are essential.

Capco's work culture is characterized by an entrepreneurial spirit and minimal bureaucracy, allowing AI Engineers to innovate swiftly. The company embraces creativity and collaboration while promoting diversity and inclusion, making it a fantastic environment for AI Engineers to thrive, grow their skills, and have a real impact on the financial services landscape.

AI Engineers at Capco should be familiar with Gen AI frameworks, deployment architectures, and technologies in the GenAI space. Proficiency in full-stack architecture, coding in cloud environments like AWS or Azure, and understanding components of prompt engineering and LLMs will be crucial for success in this role.

Capco offers significant growth opportunities for AI Engineers, including rapid learning experiences, mentorship, and the chance to tackle challenging projects. The company promotes from within and encourages a culture where individuals can explore their potential and advance their careers quickly.

When discussing your experience with agile methodologies, consider giving specific examples of past projects where you collaborated in cross-functional teams, adapting to changing requirements and focusing on delivering value. Emphasize your role and how agile processes fostered creativity and efficiency.

In your answer, outline any specific GenAI projects you've worked on, highlighting the technologies you used and your contributions to deploying solutions. Discuss how you evaluated different models and frameworks and the outcomes or impact your work had on the project.

Describe your collaborative process, emphasizing communication skills and the importance of understanding business needs. Illustrate how you build relationships with stakeholders and ensure that technical solutions align with business goals, mentioning tools or methods you use to facilitate this collaboration.

Reflect on a specific challenge related to developing AI solutions, mentioning how you addressed it. Discuss the importance of problem-solving, learning from setbacks, and how you ensured the project's success despite obstacles.

Mention the resources you rely on, such as industry journals, conferences, online courses, or communities. Emphasize your commitment to continuous learning and how you apply new insights to enhance your contributions as an AI Engineer.

Provide information on your proficiency with these languages, including specific projects where you've applied them. Mention frameworks or libraries you're comfortable with and how you utilize REST APIs in your work to develop robust applications.

In your response, discuss your understanding of prompt engineering principles and how you've applied them successfully in previous roles. Describe how you measure the effectiveness of different prompts and the iterative process you employ to refine them.

Explain your approach to evaluating costs versus benefits, including any specific metrics you track. Discuss scenarios where your insights led to sound recommendations for adopting or modifying GenAI solutions based on cost-effectiveness.

Share a brief narrative of a GenAI project you led, touching on your role, the responsibilities you held, and the project's impact. Be sure to highlight your project management skills and how you navigated the complexities involved.

Highlight your familiarity with cloud platforms like AWS or Azure, mentioning concrete examples of how you've leveraged these environments for developing AI solutions. Discuss benefits like scalability, security, and collaboration that cloud technologies offer.