AI Engineer / Research Scientist

As an AI Engineer / Research Scientist at Qeen.ai, you will develop and optimize advanced AI/ML models tailored for e-commerce personalization, focusing on recommendation engines and targeted advertising. Your role includes conducting applied research in deep learning, natural language processing, and computer vision. You will implement scalable AI architectures and collaborate with various teams to integrate your models into production systems, ensuring they are designed for real-time inference and efficiency.

Candidates for the AI Engineer / Research Scientist role at Qeen.ai should possess at least 5 years of experience in AI or machine learning engineering or research, especially within e-commerce or ad tech realms. A solid understanding of deep learning frameworks, proficiency in programming with Python and SQL, and familiarity with MLOps practices are essential. An educational background in Computer Science or a related field is also required, ideally up to a Master’s or PhD level.

An AI Engineer / Research Scientist at Qeen.ai should be skilled in deep learning frameworks such as TensorFlow, PyTorch, or JAX. Additionally, familiarity with cloud-based AI services like AWS SageMaker, Google Vertex AI, or Azure ML is beneficial. Proficiency in data science libraries including Pandas, NumPy, and Scikit-learn is crucial for managing large datasets and developing efficient AI algorithms.

At Qeen.ai, we deeply value innovation and encourage our AI Engineer / Research Scientist to stay ahead of the curve. You will be expected to engage in cutting-edge research, exploring the latest trends and advancements in AI. This includes publishing findings, implementing state-of-the-art techniques, and using your insights to drive product development that enhances our AI-driven commerce solutions.

The work environment at Qeen.ai is dynamic, collaborative, and centered around growth. As an AI Engineer / Research Scientist, you'll thrive in a high-growth startup atmosphere where your contributions make a real impact. We promote open communication, value your input, and foster a culture where your professional development is prioritized, ensuring that you can advance your career while making a substantial difference in intelligent commerce.

When answering this question, share specific projects where you utilized these frameworks. Highlight the types of models you built, the challenges you faced, and how you overcame them. Discuss the outcomes of these projects and any performance improvements achieved through your work.

To effectively respond, explain the methodologies you use for optimizing models like quantization or pruning. Discuss your experience with cloud services and MLOps practices that allow for seamless deployment and scalability, detailing how they've led to improved performance and user experience.

Share your process for handling datasets, including how you address issues like overfitting or underfitting. Explain your use of cross-validation, metrics for model evaluation, and how you iteratively refine your models to achieve higher accuracy.

Preparation is key for this question; provide a concrete example where you applied NLP or computer vision techniques. Describe the project scope, your role, the challenges you encountered, and the results achieved. Emphasize your contribution and what you learned during the project.

Discuss the importance of version control, maintaining reproducible models, and automating deployment processes. Share your experiences with CI/CD pipelines and any tools you prefer, as well as how these practices help in maintaining the integrity and performance of AI systems.

Mention your strategies for staying informed, such as following key publications, attending conferences, joining professional networks, or collaborating with academic institutions. Highlight any influential papers or trends that have inspired your recent work.

Emphasize that collaboration is essential in multidisciplinary environments. Provide examples of how you have worked closely with data scientists, engineers, and product teams to successfully integrate AI models into projects. This shows your understanding of the importance of teamwork in achieving results.

Reflect on specific deployment challenges such as latency issues, data drift, or integration bugs. Explain your approach to troubleshooting and resolving these problems, showcasing your analytical skills and persistence.

Share a story demonstrating flexibility and problem-solving skills. Detail the original approach, the factors that necessitated a change, how you adapted, and the outcome. This highlights your resilience and ability to think on your feet.

Articulate your vision of future AI developments, such as advancements in personalization, augmented reality shopping, or ethical AI practices. This shows your forward-thinking mindset and interest in how technology will shape the e-commerce landscape.