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Tier-1 brand, mid-level ML title, metro location, and generalist requirements drive high candidate competition.
Specialized ML engineering, deep-learning model building, and production MLOps make transferable backgrounds limited.
Explicit 4+ years, mandatory deep-learning frameworks, distributed training, and production MLOps enforce high shortlisting strictness.
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Own end-to-end development of deep learning models, including custom architectures for behavioral event sequences, from data tokenization to production deployment.
Build and optimize scalable ML systems and feature pipelines, ensuring inference performance and integrating MLOps practices like CI/CD and automated retraining.
Lead adoption of ML strategy within the team including model training, evaluation, deployment, and mentorship of junior engineers to shape team's ML capabilities.
Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or related field.
4+ years of professional experience building and deploying ML solutions at scale.
Strong programming skills in Python with hands-on experience in PyTorch, TensorFlow, or similar frameworks.
Deep understanding of ML lifecycle including data collection, model optimization, inference efficiency, and production system integration.
Experienced in developing and scaling deep learning models, particularly with transformer and attention-based architectures for sequence data.
Skilled in building production-grade ML systems with competencies in MLOps, distributed training, and system optimization.
Able to drive ML adoption and innovation in a cross-functional team, shaping long-term team capability rather than delivering individual models.