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Tier-1 brand, popular ML role, mid-level range, and Bangalore metro boost candidate competition.
Role requires deep ML and production model-building expertise, limiting cross-industry transferability.
Explicit 4–8 years requirement plus mandatory ML frameworks, distributed training, and production MLOps.
Own design and deployment of scalable deep learning models including custom transformers for long behavioral event sequences to drive revenue and growth.
Build and optimize feature pipelines and infrastructure (Databricks, Spark, GPU training) for efficient model training, inference, and MLOps practices.
Set the ML approach and capabilities for the team impacting enterprise business with models for conversion, anomaly detection, personalization, and closed-loop learning.
Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science or related field.
4-8 years of professional experience building and deploying large-scale ML solutions.
Strong programming skills in Python and deep expertise with PyTorch, TensorFlow, or similar frameworks.
Experience with end-to-end ML lifecycle: data processing, model training, optimization, deployment, and monitoring.
Experienced ML engineer comfortable leading foundational ML architecture and best practices for a high-value business platform.
Strong in developing and deploying custom transformer-based models for long sequence behavioral data and efficient GPU training.
Skilled in integrating ML into production systems with a focus on performance, scalability, and cross-functional collaboration.