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Known employer, metro location, mid-level role but specialized MLOps skills moderate applicant density.
Requires deep ML engineering and MLOps expertise, limiting cross-industry transferability.
Explicit 6–9 years and mandatory production MLOps/ML engineering skills create strict technical filters.
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Translate advanced data science research and algorithms into scalable, production-grade ML software handling millions of requests with low latency.
Architect and lead the development of end-to-end machine learning pipelines including data preprocessing, model generation, deployment, and monitoring.
Own full feature lifecycle from requirement gathering to deployment while driving cross-functional technical alignment and innovation.
6–9 years of professional experience in software engineering and machine learning development.
Degree in Computer Science, Artificial Intelligence, or a related quantitative field.
Proven experience in building and maintaining large-scale production ML solutions.
Strong expertise in ML model lifecycle management, scalable pipeline and API design, and system monitoring.
Experienced in bridging theoretical ML research and production implementation with measurable impact on scalability and reliability.
Skilled in technical project leadership including rapid prototyping, cross-functional collaboration, and architectural design from scratch.
Practiced in operating high-performance ML services with focus on latency, throughput, and operational telemetry.