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Strong Tier-1 brand, metro role, and broad AI/platform skill requirements increase candidate competition.
Specialized AI platform and MLOps expertise reduces cross-industry transferability.
Multiple explicit years requirements and a broad mandatory AI/platform tech stack make filters strict.
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Lead design, development, and operation of AI/ML platform capabilities and frameworks that serve multiple engineering teams, focusing on reusable, high-quality, and scalable solutions.
Own platform architecture integrity, dependency management, instrumentation, and compliance with enterprise production standards alongside SRE teams.
Drive rapid experimentation and adoption-focused delivery of platform features, collaborating cross-functionally to ensure secure, compliant, and operable platform infrastructure.
Bachelor’s degree in Computer Science, Software Engineering, Data Science, Machine Learning, or related field; experience prioritized over degree alone.
10+ years software and platform engineering experience, including mandatory hands-on experience with Python and Performance Engineering.
At least 3+ years experience in designing, building, and operating AI/ML platforms/tooling, including MLOps/LLMOps, model serving, vector infrastructure, and instrumentation.
3+ years cloud-native engineering experience on Azure, AWS, or GCP including AI/ML services and container orchestration; 1+ years leadership experience establishing engineering standards and mentoring teams.
Technically hands-on leader familiar with distributed systems and modern AI/ML platform infrastructure who treats platform as a product and drives adoption across teams.
Experienced in rapid, lean delivery cycles prioritizing operability, instrumentation, compliance, and continuous improvement within cross-functional settings including SRE and security.
Strong communicator able to translate complex technical concepts and business context into reusable frameworks and scalable solutions while mentoring others.