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Tier-1 brand, mid-level ML role, metro location, and broad MLOps/ML skillset increase competition.
Specialized ML engineering, MLOps, and distributed systems expertise reduces cross-industry portability.
Explicit years requirement and many mandatory ML, MLOps, big-data, and cloud technologies raise screening strictness.
Design, build, and operate scalable backend and ML systems for sponsored experiences, ranking, retrieval, and personalization at eBay scale.
Develop, own, and maintain MLOps pipelines for CI/CD, training, validation, and monitoring of production machine learning models, including Generative AI models and LLMs.
Collaborate with Applied Researchers and Engineering teams to translate prototypes into robust, low-latency, production-ready systems supporting millions of users.
MS in Computer Science or related field with 5+ years relevant experience, or BS/BA with 6+ years in ML/AI/Data Engineering.
Expertise in production engineering and software development in an object-oriented language (Scala, Java, Python, etc.).
Experience with big data distributed processing frameworks (Apache Hadoop, Spark, Flink) and ML frameworks like TensorFlow or PyTorch in production.
Proven ability to build and manage CI/CD pipelines, containerization (Docker, Kubernetes), scalable distributed systems, and APIs; experience with cloud services (AWS, GCP, Azure).
Experienced in operationalizing machine learning models, including state-of-the-art Generative AI/LLMs, in live, 24/7 production environments with monitoring and incident response.
Demonstrates strong cross-functional collaboration skills with applied researchers and product managers to deploy ML research into production.
Comfortable building and scaling big data pipelines and ML infrastructure to serve millions of users with low latency and high throughput.