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Tier-1 brand, metro location, and common ML title increase competition despite senior specialization.
Core ML skills are transferable, but fraud/behavioral modeling and large-model training add domain specificity.
Explicit 8+ years and deep learning, distributed training, and production ML requirements make filters strict.
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Build and develop custom transformer-based deep learning models from scratch to detect fraud and misuse in Adobe's ecosystem.
Manage end-to-end ML lifecycle including data feature engineering, architecture design, large-scale GPU training, deployment, and monitoring of models.
Translate prototypes into scalable, reliable, and optimized production ML systems and contribute to MLOps practices (experiment tracking, model versioning, CI/CD, retraining, monitoring).
Bachelor’s or Master’s degree (or equivalent) in Computer Science, Machine Learning, Data Science, or related field.
8+ years professional experience building and deploying ML solutions at scale.
Strong programming skills in Python with experience in PyTorch, TensorFlow, or equivalent frameworks.
Deep knowledge of ML lifecycle, model optimization, inference efficiency, and production system integration.
Experienced in developing advanced deep learning architectures, especially custom transformers and attention-based models for long sequential data.
Skilled in GPU-based training optimizations including mixed-precision and distributed training techniques.
Experience with behavioral modeling, fraud detection, or anomaly detection domains is a major plus.