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Tier-1 employer, metro location, mid-level ML role with broad infra and MLOps requirements.
Requires specialized generative AI and MLOps expertise, reducing cross-industry transferability.
Explicit 4+ years ML engineering, required cloud/Kubernetes and MLOps expertise.
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Design and deliver scalable generative AI services integrated with multiple applications and tenants, running at production scale.
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 translate customer requirements into production-ready AI solutions and prototypes.
4+ years of industry experience in ML engineering developing AI systems and services.
Experience designing and building distributed microservices on public clouds like AWS or GCP.
Proficiency with containerized deployment technologies such as Kubernetes and Spinnaker.
Experience working with distributed systems and data frameworks like Kafka, Spark, Docker, and Hadoop.
Strong background in building and operating scalable AI/ML systems in cloud-native environments.
Experience partnering cross-functionally with product, research, and architecture teams to deliver innovative AI products.
Proven track record of delivering large-scale AI services with operational ownership and problem-solving for novel challenges.