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We are looking for a Lead AI Engineer to drive end-to-end ML delivery, from problem framing and data sourcing to deployment and monitoring. You will design scalable batch and real-time ML architectures, ship production-grade models across multiple use cases, and partner with engineering, product, and business stakeholders to define KPIs and outcomes.
Responsibilities
- Lead the full ML system lifecycle: problem formulation, data collection, feature engineering, model training, evaluation, deployment, and monitoring
- Design and implement scalable ML architectures that support batch and real-time pipelines
- Handle large-scale structured and unstructured datasets including clickstream, transactional, text, and image data
- Build and ship models such as recommendation systems, ranking models, forecasting models, and classification/regression systems
- Apply deep learning methods including neural networks, transformers, and representation learning when appropriate
- Create and maintain feature stores, model registries, and experiment tracking systems
- Ensure production readiness by optimizing latency, scalability, reliability, and cost efficiency
- Partner with engineering, product, and business teams to define KPIs and success metrics
Requirements
- 5+ years of experience building and deploying ML systems in production environments
- Hands-on expertise designing scalable ML architectures, including batch and real-time pipelines
- Strong ability to work with large-scale structured and unstructured datasets, including clickstream, transactional, text, and image data
- Demonstrated experience developing recommendation systems, ranking models, forecasting models, and classification/regression systems
- Deep understanding of deep learning techniques including neural networks, transformers, and representation learning
- Practical skills building and maintaining feature stores, model registries, and experiment tracking systems
- Solid knowledge of production readiness topics such as latency optimization, scalability, reliability, and cost efficiency
- Proven collaboration skills with engineering, product, and business teams to define KPIs and success metrics
- English proficiency at B2 (Upper-Intermediate) level or higher