Role Summary

We are seeking an AI/ML Engineer with ~5–6 years of experience to develop and manage the generative AI models that power our OCM platform. In this role, you will develop and maintain AI pipelines for content generation and evaluation, working with state-of-the-art ML tools. You will ensure our AI models produce high-quality, multilingual content and continuously improve through feedback loops.

Responsibilities

•Design and implement end-to-end generative AI pipelines for creating personalized marketing content. This includes data preprocessing, model inference, and post-processing of outputs for quality assurance.

•Leverage pre-trained models (e.g., via Azure OpenAI or Hugging Face models) and customize them for the domain needs. 

•Create mechanisms to automatically score or classify generated content for relevance, quality, and compliance. Implement evaluation workflows to filter out inappropriate or low-quality AI-generated content before it reaches end-users.

•Incorporate feedback from users and domain experts into the model improvement process. For example, implement prompt adjustments, model retraining, or rules based on what content performs best, to continuously refine output.

•Ensure the AI pipeline can generate content in multiple languages. Adapt models or use translation services as needed so content stays accurate and contextually appropriate across languages.

•Deploy models and AI services into production (using Azure services like Azure Functions or container deployments). Monitor model performance (latency, accuracy) and optimize as required. Troubleshoot and resolve issues in model inference in production environments.

•Work closely with the cross functional teams to develop AI capabilities into the application.

•Stay updated with the latest developments in AI/ML (new models, techniques, prompt engineering strategies) and experiment with improvements that could enhance our product’s intelligence and efficiency.

•Document model architectures, experiment results, and maintain version control for models and data (ensure we know which model version is in production). Follow best practices for model lifecycle management.