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Tier-1 brand, remote role, and popular ML engineering title increase applicant competition.
Requires deep ML production skills and domain-specific tooling, limiting cross-industry transferability.
Explicit 7+ years, mandatory production ML experience and framework requirements enforce strict filters.
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Scope, design, and deploy scalable machine learning systems in production with end-to-end ownership.
Partner with product and engineering teams to execute AI/ML product roadmaps addressing customer needs at a global scale.
Build and maintain data pipelines and ML models, driving high engineering standards including mentoring and best practices across teams.
7+ years of applied Machine Learning experience with strong proficiency in Python.
Experience building, shipping, and maintaining ML models in production in fast-paced, ambiguous environments.
Familiarity with frameworks such as PyTorch, TensorFlow, or Keras and ML Ops concepts like testing, monitoring, and retraining models.
Experience with big data technologies (e.g., Kafka, Spark, Hadoop, DynamoDB) and agile team environments; Location: Remote India.
Deep ML engineering expertise demonstrated by ownership of large scale ML solutions from design through deployment.
Ability to operate effectively across product, engineering, and data platform teams in ambiguous, rapidly evolving settings.
Experience architecting experiments and analyses that directly inform product roadmaps and business decisions.