AI Engineer

New Feature Development: Design, implement, and deploy new features using Python to enhance our core AI functionalities.

DevOps and MLOps: Contribute to the development and maintenance of our CI/CD pipelines and MLOps infrastructure. This includes automating model training, deployment, and monitoring processes to ensure product resilience and scalability.

In-house Evaluation System: Develop and improve our internal evaluation system to accurately measure model performance, track key metrics, and ensure data integrity.

Integration and Deployment: Manage the integration of our AI solution into client environments.

AI Model Improvement: Work with our existing models to enhance their performance and efficiency. This will include tasks like fine-tuning large language models and applying advanced prompt engineering techniques.

R&D Contributions: Participate in short-term research and development projects to explore new technologies and approaches that can give our product a competitive edge.

Product Hardening: Take ownership of tasks focused on making the product production-ready, including improving reliability, enhancing performance, and implementing robust error handling and monitoring.

DevOps and MLOps: Managing CI/CD pipelines and automating model deployment.

Client Integration: Working on client-side integration

Evaluation Systems: Developing in-house systems to monitor and measure model performance.

AI/ML Techniques: Applying fine-tuning and prompt engineering to enhance model performance.

Production Readiness: Making the product resilient and ready for production, including performance optimization and robust error handling.