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Metro location and known brand increase applicants, senior ML platform specialization limits qualified candidate pool.
High—role demands specialized ML platform, LLM, and MLOps experience, limiting cross-industry transferability.
High due to explicit 8+ years, 3+ years management, and mandatory ML/MLOps, LLM, and cloud skills.
Lead design, development, and operationalization of a unified AI/ML data collection platform supporting scalable data pipelines, model lifecycle management, and evaluation frameworks.
Provide technical and team leadership to build production-grade ML systems leveraging LLMs, MLOps tools, and cloud-native architectures.
Collaborate cross-functionally to align ML platform capabilities with business objectives, drive engineering best practices, and ensure platform reliability and governance.
Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Data Science, or related field.
8+ years in software engineering focused on ML systems, ML platforms, or distributed systems.
3+ years managing engineering teams and leading technical initiatives.
Hands-on experience with MLOps tools, pipeline orchestration (e.g., Airflow, Kafka), Python/SQL programming, cloud platforms (AWS/GCP/Azure), containerization (Docker, Kubernetes), and production LLM-based systems.
Experienced engineering manager with deep expertise in building and scaling production ML platforms, especially involving LLM and AI-driven data workflows.
Technical leader capable of solving complex system-level challenges and delivering scalable, reliable solutions in data-driven environments.
Proficient collaborator who can work across global teams and partner closely with product, data engineering, and research to align technology with business goals.