Group Sales Representative in Noida

The Lead – Asset Management will be responsible for managing the group’s hospitality assets to ensure optimal financial performance, operational efficiency, and alignment with the organization’s strategic vision. The role will drive value creation across owned assets, oversee day-to-day hotel operations, ensure compliance with brand standards, manage procurement and contracts, and coordinate design and operational initiatives with operators and internal teams Source: Adani Group | Job ID: 53465

About Paytm Group: Paytm is India's leading mobile payments and financial services distribution company. Pioneer of the mobile QR payments revolution in India, Paytm builds technologies that help small businesses with payments and commerce. Paytm’s mission is to serve half a billion Indians and bring them to the mainstream economy with the help of technology. About the Team: Our Key Offerings are divided into 5 broad categories as follows: • Entertainment • Digital Platforms • CVM Solutions • Enterprise Services • Financial Platforms One97 has the widest and largest deployment of telecom applications on cloud platforms in India and has a myriad of VAS services that have helped operators augment their revenue even in complex markets like India, SAARC, Middle East, Africa and many more Role Overview: We are looking for a data-driven Growth to support our mobile app and SuperApp ecosystem. The role combines analytical rigor with hands-on execution, focusing on user acquisition, retention, engagement, and GTM strategy optimization. The ideal candidate will be comfortable analyzing campaigns, evaluating go-to-market plans, and identifying actionable opportunities for growth. Key Responsibilities: *Growth Analysis & Insights: -Analyze campaign performance (push notifications, in-app messages, email, SMS, partnerships) and track conversion KPIs. -Monitor retention, reactivation, and engagement metrics, including feature adoption, transaction frequency, and monetization flows. -Conduct funnel and cohort analyses to identify bottlenecks in user journeys and growth opportunities. -Track campaign ROI, cost per acquisition (CPA), activation rate, referral conversion, and other key growth indicators. *GTM Strategy Support: -Assist in designing, executing, and analyzing go-to-market plans for new features, wallet flows, or app integrations. -Evaluate the effectiveness of promotional campaigns, incentive programs, and partnerships in driving adopti

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 distributed strategies (DDP, FSDP, or equivalent). Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs. Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization. Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and

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 distributed strategies (DDP, FSDP, or equivalent). Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs. Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization. Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and