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Tier-1 brand, generic software title, mid-level requirement, and Bengaluru metro raise candidate competition.
Requires ML engineering and MLOps expertise, moderately transferable but needs domain-specific experience.
Explicit 4+ years plus mandatory ML, cloud, Kubernetes, and MLOps experience creates strict screening filters.
Design and deliver scalable generative AI services integrated across multiple applications and tenants, running at production scale.
Drive system efficiencies such as capacity planning, performance tuning, monitoring, root cause analysis, and automation.
Collaborate with Product Managers, Architects, Data Scientists, and Deep Learning Researchers to translate customer requirements into production-ready AI technologies.
4+ years industry experience in ML engineering building AI systems and/or services.
Experience designing and building distributed microservices on public clouds like AWS or GCP.
Proficiency with containerized deployments using Kubernetes, Spinnaker or equivalent.
Work Experience Required: 4+ years ML engineering experience.
Experienced in distributed, scalable systems and modern data processing frameworks (Kafka, Spark, Hadoop, Docker).
Demonstrated ownership and ability to innovate at large scale within cross-functional teams.
Familiarity with MLOps workflows and machine learning frameworks such as Tensorflow, PyTorch, SageMaker, or similar.