Responsibilities
- Design and deploy scalable, high-performance machine learning systems in production with reliability and efficiency from initial implementation
- Develop and operationalize supervised, unsupervised, deep learning, and generative AI models at scale in live environments
- Lead technical decisions for ML pipelines, feature storage, training frameworks, and inference platforms, balancing performance, cost, and long-term maintainability
- Create and manage retrieval-augmented generation systems, fine-tuning workflows, prompt engineering strategies, and evaluation frameworks for large language model applications
- Implement continuous integration and deployment for machine learning, including model version control, monitoring, drift detection, and automated retraining
- Optimize inference speed, model accuracy, system reliability, and operational costs across deployed AI systems
- Partner with product, engineering, and data teams to convert business challenges into scalable artificial intelligence solutions
- Guide junior and mid-level engineers, define best practices, and shape team-wide technical standards
- Support strategic planning in data infrastructure, AI platform development, and cloud technology direction
- Utilize mobile analytics and engagement data from sources like Adjust, MoEngage, and Firebase for use cases including churn modeling, personalization, and marketing optimization
Other
- This organization promotes equal employment opportunities and evaluates candidates based on merit, regardless of background.
- Applications are encouraged from individuals of all identities, with no discrimination related to nationality, gender, age, religion, or disability.
