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Tier-1 brand, Bangalore metro, and mid-level ML engineering role drive high competition.
Specialized ML engineering, MLOps, and distributed systems experience significantly limits cross-industry transferability.
Explicit 4+ years ML engineering requirement plus mandatory cloud, Kubernetes, and ML infra skills raises strictness.
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Design and deliver scalable generative AI services integrated with multiple applications for thousands of tenants in production.
Drive system efficiencies via automation including capacity planning, configuration management, performance tuning, monitoring, and root cause analysis.
Collaborate with Product Managers, Architects, Data Scientists, and Researchers to design prototypes and transition innovative AI technologies into production.
Minimum 4 years industry experience in ML engineering building AI systems and/or services.
Experience designing and building distributed microservices on AWS, GCP, or comparable public cloud platforms.
Proficiency with containerized deployment stacks such as Kubernetes and Spinnaker.
Experience with distributed scalable systems and modern data frameworks like Kafka, Spark, Docker, Hadoop.
Experienced in operating and delivering large-scale machine learning and AI production systems with measurable impact.
Comfortable working in cross-functional teams including data scientists and researchers to innovate and deploy ML models.
Ability to handle on-call rotations and proactively resolve critical system issues through automation and tuning.