Applied AI Manager

As an Applied AI Manager at Careem, you will be responsible for leading AI-first process redesign initiatives across various domains, including Care, Ops, Finance, HR, and Product. You will architect LLM-based productivity tools tailored to team-specific workflows and oversee the translation of unstructured processes into intelligent automations. Additionally, you'll partner with engineers to develop custom AI pipelines while defining success metrics for AI projects.

To become an Applied AI Manager at Careem, candidates should have 3-5 years of relevant experience in transformation, process engineering, or operating model design, ideally within tech-forward or consulting environments. Strong expertise in LLM stack architecture and the ability to drive innovative AI productivity tools are also crucial. Analytical thinking and a builder/operator mindset will set successful candidates apart.

Careem is dedicated to its employees' growth and development. As an Applied AI Manager, you will work alongside inspiring colleagues and gain exposure to cutting-edge technologies. You will also have opportunities for continuous learning and growth, enabling you to drive meaningful impact within the organization and in the broader region.

An Applied AI Manager at Careem should have familiarity with LLM stack architecture, model selection processes, prompt chaining, and vector database integration. Knowledge of multi-agent workflows and systems thinking to map process bottlenecks to AI powers is also essential in this role.

Careem offers a range of benefits for the Applied AI Manager position including a flexible work arrangement of four days in the office and one day from home. Colleagues can work remotely from any country for 30 days a year, alongside unlimited vacation days. Moreover, healthcare benefits and fitness reimbursements are provided to support employee well-being.

When asked this question, focus on a specific project where you implemented AI solutions. Highlight the objectives, your role, the technologies used, and the measurable outcomes such as improved efficiency or cost savings. Show how your leadership and technical skills made a positive impact on the organization.

Your answer should illustrate a systematic approach. Discuss how you analyze existing processes, identify bottlenecks, and determine the best AI tools or methodologies to enhance efficiency. Emphasize collaboration with stakeholders and the importance of obtaining buy-in from the team during the redesign process.

Here, you want to demonstrate your technical knowledge. Discuss specific LLM models you have worked with, your understanding of model selection, and experiences with setting up prompt chaining or vector databases. Provide examples that relate to your capacity to improve productivity through LLM tools.

In your response, mention important KPIs and metrics you use to evaluate AI initiatives. Include metrics like ROI, user satisfaction, and usage patterns. Sharing a past experience where you successfully implemented a measurement strategy will make your answer robust.

Reflect on obstacles like data quality, team resistance, or technology limitations. Share specific actions you took to address these challenges, such as stakeholder engagement or flexible problem-solving approaches. Highlight the lessons learned from these experiences.

Discuss your familiarity with Agile frameworks and their relevance to AI project management. You might mention how you have used Agile to promote iterative development, facilitate team collaboration, and adapt to changing project needs. Provide examples to illustrate your experience.

Here, you can discuss the importance of managing change effectively to ensure user adoption and success for AI projects. Highlight strategies such as training sessions, clear communication, and creating supportive cultural environments that embrace innovation.

Focus on your communication skills and ability to bridge gaps between diverse teams. Share your strategies for facilitating discussions, workshops, and ensuring that both technical jargon and business goals are understood across the board.

Emphasize the significance of ethics in AI. Discuss how you conduct assessments for fairness, transparency, and accountability. Mention any specific frameworks you are familiar with and examples of how you've integrated ethical practices into AI projects.

Offer insights based on current trends, such as increased automation, personalized customer experiences, or enhanced decision-making. You may also want to talk about the importance of continuous learning and adaptation in this fast-evolving field.