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Tier-1 brand, metro Bangalore, mid-level (4+ years), and broad ML/MLOps skillset.
Requires specialized ML engineering and MLOps experience, so cross-industry transferability is limited.
Explicit 4+ years plus mandatory ML engineering, cloud, Kubernetes, and MLOps requirements.
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Design and deliver scalable generative AI microservices integrated across thousands of tenants in production environments.
Drive system efficiencies through automation in capacity planning, configuration management, performance tuning, monitoring, and root cause analysis.
Collaborate closely with Product Managers, Application Architects, Data Scientists, and Deep Learning Researchers to translate customer requirements into prototypes and production-ready AI solutions.
Minimum 4+ years industry experience in ML engineering building AI systems or services.
Proven expertise in designing and deploying distributed microservices on public cloud platforms like AWS or GCP.
Hands-on experience with containerized deployment workflows using Kubernetes and related tools (e.g., Spinnaker).
Work Experience Required: Minimum 4+ years in relevant ML engineering roles.
Experienced in operating and delivering innovative, large-scale distributed AI services with strong ownership and collaboration skills.
Technically skilled in distributed data frameworks such as Kafka, Spark, Hadoop, and container/Docker ecosystems.
Familiarity with ML infrastructure and workflows including MLOps, and practical exposure to platforms like TensorFlow, PyTorch, Sagemaker, or equivalent machine learning technologies.