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Tier-1 brand, Bangalore metro, and mid-level ML role drive high candidate competition.
Specialized ML engineering and MLOps skills make cross-industry fit moderately sensitive.
Mandatory 4+ years ML engineering plus specific cloud and MLOps stack increases shortlisting strictness.
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Design and deliver scalable generative AI services integrated with multiple applications and thousands of tenants in production.
Drive system efficiencies through automation including capacity planning, configuration management, performance tuning, and root cause analysis.
Collaborate with Product Managers, Architects, Data Scientists, and Researchers to translate customer requirements into prototypes and production solutions.
4+ years of industry experience in ML engineering building AI systems and/or services.
Experience designing and building distributed microservices on AWS, GCP or other public cloud platforms.
Experience with containerized deployment using Kubernetes, Spinnaker, and related technologies.
Work Experience Required: 4+ years in relevant ML engineering roles.
Proven ability to operate and deliver results at large scale in distributed, scalable systems using Kafka, Spark, Docker, Hadoop, etc.
Strong ownership and leadership capabilities demonstrated through driving innovation and collaboration.
Experience or knowledge of MLOps workflows and large-scale machine learning technologies (e.g., Sagemaker, Tensorflow, Pytorch, Triton, Spark).