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Tier-1 brand, metro Bengaluru location, and mid-level ML/cloud role increase candidate competition.
Requires specialized ML engineering and MLOps cloud experience, limiting cross-industry transferability.
Explicit 4+ years, mandatory ML engineering, cloud, and MLOps tech requirements.
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Design, develop, and deliver scalable generative AI microservices for multi-tenant, production environments.
Automate system operations including capacity planning, configuration, tuning, monitoring, and root cause analysis.
Collaborate with Product Managers, Application Architects, Data Scientists, and Deep Learning Researchers to translate customer requirements into production AI applications.
4+ years of ML engineering experience in building AI systems or services.
Experience with distributed microservices architecture on AWS, GCP, or other public clouds.
Proficiency with container orchestration and deployment tools like Kubernetes and Spinnaker.
Work Experience Required: 4+ years in relevant ML engineering roles.
Experienced in building and operating large-scale, distributed AI and ML systems leveraging technologies such as Kafka, Spark, Hadoop, Docker.
Strong ownership mindset combined with ability to collaborate across cross-functional teams including technical and business stakeholders.
Familiarity with MLOps workflows and machine learning frameworks like TensorFlow, PyTorch, SageMaker, or equivalent large-scale ML technologies.