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High — Tier-1 brand, Bangalore location, mid-level ML role with broad deep-learning requirements.
Medium — core ML skills transfer, but behavioral fraud and large-scale GPU production experience narrows fit.
High — explicit 5+ years plus mandatory deep-learning, distributed training, and production deployment skills.
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Build and develop deep learning models from scratch, including custom transformer and attention-based architectures for fraud detection and behavioral modeling.
Manage full ML model lifecycle: feature engineering, architecture design, large-scale GPU training, deployment, and monitoring to protect Adobe's ecosystem.
Translate prototypes into scalable, reliable, and observable production ML systems, optimizing inference performance and contributing to MLOps practices such as CI/CD and automated retraining.
Bachelor’s or Master’s degree (or equivalent) in Computer Science, Machine Learning, Data Science, or related field.
5+ years of professional experience building and deploying ML solutions at scale.
Proficient in Python and experienced with PyTorch, TensorFlow, or similar frameworks.
Deep understanding of end-to-end ML lifecycle including data collection, deployment, optimization, and production monitoring.
Experienced in designing and training custom large-scale deep learning models, particularly transformer-based architectures for long behavioral sequence data.
Strong background in production ML systems engineering with expertise in model optimization, inference efficiency, and distributed training on GPU infrastructure.
Domain experience or interest in fraud detection, anomaly detection, or behavioral modeling within high-impact, security-sensitive environments.