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Tier-1 brand, popular ML title, mid-level experience, metro location, and broad skill requirements raise competition.
Core ML skills are transferable, though creative-marketing domain reduces cross-industry fit to medium.
Explicit years, mandatory production ML, PyTorch, Spark, Kubernetes and GPU requirements create high shortlisting strictness.
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Own end-to-end development, training, deployment, and iteration of machine learning models and pipelines for creative understanding across vision, video, and language.
Build and deliver scalable ML pipelines for feature engineering, training, versioning, inference, and production monitoring in Spark, Kubernetes, and GPU/CPU environments.
Collaborate cross-functionally with product managers, research scientists, and engineers to link ML work directly to customer value such as performance insights and recommendations.
Master's degree with 5+ years relevant experience or PhD with 2+ years relevant experience or equivalent impact.
Proven experience shipping ML models to production with measurable product or business impact.
Strong skills in Python and PyTorch for model development and deployment.
Experience in deep learning for vision, video, NLP, or generative AI, and working knowledge of distributed data processing and cloud platforms like Spark, Kubernetes, GCP, AWS, or Azure.
Operates independently with ownership of well-defined, complex ML problems, from modeling approach through deployment and iteration.
Strong strategic fit in building end-to-end ML systems tied to marketing or creative content, with experience in recommendation systems a plus.
Comfortable working across diverse technical and product teams with clear communication on ML trade-offs and technical decisions.