Back to SearchSenior AI Engineer
Remote in Argentina, & 4 others
Data Science& 7 others
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We are seeking a Senior AI Engineer to own the end-to-end lifecycle of ML systems, from problem formulation and data collection through model training, deployment, and monitoring. This role involves designing scalable architectures and deploying production-grade models across a variety of use cases while collaborating closely with cross-functional teams.
Responsibilities
- Own end-to-end lifecycle of ML systems: problem formulation, data collection, feature engineering, model training, evaluation, deployment, and monitoring
- Design and implement scalable ML architectures including batch and real-time pipelines
- Work with large-scale structured and unstructured datasets such as clickstream, transactional, text, and image data
- Develop and deploy models such as recommendation systems, ranking models, forecasting models, and classification/regression systems
- Apply deep learning techniques including neural networks, transformers, and representation learning where appropriate
- Build and maintain feature stores, model registries, and experiment tracking systems
- Ensure production readiness through latency optimization, scalability, reliability, and cost efficiency
- Collaborate with engineering, product, and business teams to define KPIs and success metrics
Requirements
- 3+ years of experience in building and deploying ML systems in production environments
- Expertise in designing scalable ML architectures, including batch and real-time pipelines
- Proficiency in working with large-scale structured and unstructured datasets, including clickstream, transactional, text, and image data
- Experience developing recommendation systems, ranking models, forecasting models, and classification/regression systems
- Knowledge of deep learning techniques including neural networks, transformers, and representation learning
- Skills in building and maintaining feature stores, model registries, and experiment tracking systems
- Understanding of production readiness considerations such as latency optimization, scalability, reliability, and cost efficiency
- Capability to collaborate with engineering, product, and business teams to define KPIs and success metrics
- English proficiency at B2 level or higher