Account Manager Enterprise in Noida

About the Role Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide. In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring. The team is actively building in-house behavioral foundation models that learn identity-preserving representations from long sequences of user activity. This is a role for an engineer who wants to own deep learning systems end-to-end — not consume pre-built ones. Key Responsibilities Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences. Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine-tuning. Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and

Role Description Responsible for the entire F&B Production Key Responsibilities Plan the kitchen operations i.e. Menu Manpower workflow procurement QHSE & training for the kitchen production team Direct the preparation seasoning and cooking of salads soups fish meats vegetables desserts or other foods. May plan and price menu items order supplies and keep records and accounts. Will work on the range with the kitchen team to train guide & develop Take charge of the assigned kitchen/s of operations Monitor sanitation practices to ensure that employees follow standards and regulations Prepare Produce and Present food to the agreed standard and quality with proper presentation and prepared to the agreed profit accordance with company policy Organize all resources within the kitchen for optimum productivity and use Prepare cook and produce food with particular emphasis on presentation hygiene and economy Plan and cost regular and party menus on weekly basis in co-ordination with the Manager / site In-charge to ensure variety and healthy options Assist Manager / site In-charge in indenting for provisions and discipline Responsible to allocate duties for all kitchen staff Responsible for implementing company discipline and HR policies Develop and train junior staff at the site to take on more responsibilities in the future Ensure proper cleanliness of the kitchen equipment and the kitchen area

About the Role Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide. In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring. The team is actively building in-house behavioral foundation models that learn identity-preserving representations from long sequences of user activity. This is a role for an engineer who wants to own deep learning systems end-to-end — not consume pre-built ones. Key Responsibilities Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences. Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine-tuning. Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and