Applied AI Specialist (NLP Focus)

As an Applied AI Specialist at Opreto, your primary responsibilities include implementing and fine-tuning NLP capabilities using advanced large language model platforms, training models with industry-specific datasets, and optimizing NLP pipelines for efficiency. You’ll work closely with the backend team to ensure seamless integration of NLP features, conduct performance evaluations, and maintain AI services in a cloud environment.

To qualify for the Applied AI Specialist role at Opreto, candidates should have over 5 years of experience in AI/NLP development with a focus on pre-built models. Proficiency in Python is essential for model training and API integration. Familiarity with modern NLP frameworks—such as Hugging Face Transformers—and deploying AI services on platforms like AWS, Google Cloud, or Azure is highly desirable.

At Opreto, we value teamwork and collaboration. As an Applied AI Specialist, you will work closely with cross-functional teams, including backend developers and product managers. This ensures that the NLP capabilities you develop are seamlessly integrated into the application and deliver the best user experience.

The Applied AI Specialist at Opreto will lead exciting projects aimed at developing NLP-driven features for a new application. This includes leveraging AI and real-time data processing to create innovative solutions that can significantly improve user engagement and effectiveness in various industries.

Opreto offers a flexible, remote-first work environment where our team thrives on creativity and innovation. As an Applied AI Specialist, you'll be part of a supportive and collaborative culture, providing you with the freedom to work from anywhere in Canada while contributing to meaningful projects.

When answering this question, focus on specific frameworks you've worked with, such as Hugging Face or spaCy. Discuss your experience in applying them to real-world problems, the projects you've completed, and how you optimized models for better performance. Highlight your approach to problem-solving and any notable results achieved.

Discuss your strategy for identifying and sourcing domain-specific datasets that enrich model training. Explain techniques such as data preprocessing, data augmentation, and how to balance datasets to enhance model accuracy. It’s also helpful to mention any challenges you've faced and how you overcame them.

Outline the metrics you use to measure model performance, such as precision, recall, F1 score, and accuracy. Explain your approach to A/B testing and continuous evaluation post-deployment to ensure the model performs well in real-time scenarios.

Discuss your experience in cloud deployment and management practices, focusing on reliability. This could include load balancing, server health monitoring, automatic scaling techniques, and strategies for updating models without downtime.

When responding, provide a clear example of a challenge related to NLP development, such as dataset limitations or model biases. Detail the steps you took to address the issue, including adjustments made to the model or data, and the outcome of your efforts.

Share your specific experiences involving real-time NLP applications, including any technologies you employed. Discuss performance challenges you faced, such as latency issues, and how you addressed them to deliver a smooth user experience.

Explain your understanding and experience with designing RESTful APIs for NLP application integration. Speak about the tools and methods you’ve used to connect APIs effectively, ensuring robust and scalable interactions between components.

Discuss various techniques for optimizing pipelines, such as model pruning, quantization, and feature engineering. Highlight your focus on achieving lower latency while maintaining high accuracy and how performance monitoring plays a role in your optimization process.

Articulate your approach to effective collaboration, emphasizing communication skills, regular check-ins, and using collaborative tools. Provide examples of previous projects where your teamwork improved project outcomes and fostered creativity.

Explain your knowledge of how audio-to-text technologies work and their relevance to NLP. Discuss any projects where you integrated audio inputs into NLP workflows, what tools you used, and the results achieved.