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We are seeking a Lead AI Engineer to manage machine learning systems from initial concept through to production launch. This role involves designing scalable ML architectures, building robust pipelines, and working with cross-functional teams to deliver effective AI solutions.
Responsibilities
- Manage the complete ML systems lifecycle, covering problem formulation, data collection, feature engineering, model training, evaluation, deployment, and monitoring
- Create and implement scalable ML architectures, including both batch and real-time pipelines
- Handle large-scale structured and unstructured datasets, such as clickstream, transactional, text, and image data
- Build and launch models like recommendation systems, ranking models, forecasting models, and classification/regression systems
- Utilize deep learning methods, including neural networks, transformers, and representation learning, as needed
- Establish and maintain feature stores, model registries, and experiment tracking systems
- Guarantee production readiness by optimizing for latency, scalability, reliability, and cost efficiency
- Partner with engineering, product, and business teams to establish KPIs and success metrics
Requirements
- Over 5 years of experience in machine learning engineering or a related discipline
- At least 1 year of applicable leadership experience
- Strong expertise in MLOps practices for overseeing the ML lifecycle
- Skilled in PySpark and Python for developing scalable data and ML pipelines
- Proven background in designing and launching production-grade ML systems
- Solid understanding of batch and real-time data processing architectures
- Experience handling large-scale structured and unstructured datasets
- English language skills at a B2 level or above
Nice to have
- Experience with Apache Kafka and MLflow
- Knowledge of machine learning concepts and PyTorch