AI Engineer

As an AI Engineer at iGenius, you will be implementing and optimizing large language models and generative AI systems for production environments. You'll work closely with researchers to convert innovative AI concepts into scalable and robust systems, while also ensuring efficient model training and fine-tuning. Your role will involve troubleshooting complex AI issues, developing APIs for integration within our product ecosystem, and staying updated with emerging AI techniques.

To qualify for the AI Engineer role at iGenius, candidates should have a Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. Additionally, at least 3 years of hands-on experience with machine learning models is required, along with strong programming skills in Python and familiarity with ML frameworks like PyTorch or TensorFlow. Experience in deploying AI models in production and knowledge of cloud platforms will also be significant advantages.

Advancing your career as an AI Engineer at iGenius involves staying current with the latest trends and technologies in AI, contributing to innovative projects, and collaborating effectively within cross-functional teams. Actively participating in internal training and upskilling sessions, building a strong network within the company, and seeking mentorship opportunities can also help you grow within the organization.

In the AI Engineer role at iGenius, responsibilities encompass implementing and optimizing AI models for production, designing infrastructure for model training, and developing APIs for AI services. You will also be tasked with monitoring model performance, contributing to internal tools, collaborating with data engineers, and ensuring adherence to responsible AI practices.

The company culture at iGenius is open, collaborative, and focused on innovation. As an AI Engineer, you'll find yourself in a supportive environment that encourages teamwork and the exchange of ideas. The organization values ongoing learning and provides opportunities to work on cutting-edge projects with leading technologies, making it an exciting place for tech enthusiasts to thrive.

In answering this question, provide specific examples of projects where you have implemented or optimized large language models. Discuss your familiarity with deployment processes, any frameworks you’ve utilized, and your approach to troubleshooting deployment issues.

To effectively answer, explain your method of analyzing model performance, the metrics you consider important, and the optimization techniques you are familiar with, such as quantization or pruning. Providing concrete examples of past successes can further strengthen your response.

Here, list the programming languages you excel in, particularly emphasizing Python, and discuss your experience with ML frameworks like PyTorch or TensorFlow. Mention any projects that highlight your proficiency and how you've leveraged these tools in your work.

In your response, outline a specific challenge, the context behind it, and the steps you took to address the issue. Highlight your problem-solving skills and any collaborative efforts that contributed to the resolution.

Explain your understanding of responsible AI practices, including bias mitigation and ethical considerations in AI development. Discuss how you integrate these practices into your workflows and decision-making processes.

Be explicit about your experience with tools like MLflow, Kubeflow, or any other ML engineering platforms. Share how you’ve utilized these tools to streamline your AI development processes and improve efficiency.

Discuss your familiarity with cloud platforms such as AWS, GCP, or Azure, and how you’ve used them in deploying AI systems. Address your experience with containerization technologies, like Docker, and the advantages they provide in AI deployments.

Share your methods for continuous learning, such as following AI research papers, attending workshops, participating in online courses, or engaging in community discussions. Emphasize the importance of staying up-to-date in this fast-evolving field.

Describe your experience working in teams with diverse skill sets and how you've contributed to achieving shared goals. Highlight any instances of successful collaborations that led to project completions or innovations.

Outline your strategy for evaluating existing systems and determining necessary updates or new features. Discuss how you prioritize tasks and collaborate with stakeholders to ensure alignment with organizational goals.