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Tier-1 brand, metro location, and mid-level ML role create high applicant competition.
ML engineering and MLOps skills are transferable across industries but require domain expertise.
Mandatory 4+ years ML engineering plus strict MLOps, cloud, and infra requirements.
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Design and deliver scalable generative AI services supporting multi-tenant applications running in production at scale.
Drive system efficiencies via automation including capacity planning, configuration management, performance tuning, monitoring, and root cause analysis.
Collaborate cross-functionally with Product Managers, Architects, Data Scientists, and Deep Learning Researchers to prototype and deploy innovative AI technologies.
Minimum 4 years of industry experience in Machine Learning engineering focused on building AI systems or services.
Experience designing and building distributed microservices on public cloud platforms such as AWS or GCP.
Proficiency in containerized deployment technologies including Kubernetes and Spinnaker.
Experience with distributed systems and data processing frameworks like Kafka, Spark, Docker, Hadoop.
Experienced in implementing, operating, and delivering large-scale innovative AI solutions in production environments.
Comfortable working in highly collaborative, cross-disciplinary teams involving ML engineering, data science, and product management.
Knowledgeable in MLOps workflows and familiar with machine learning frameworks such as Tensorflow, Pytorch, and ML infrastructure tools.