Sr Machine Learning Engineer

PayPal is seeking a Senior Machine Learning Engineer to deploy, monitor, and maintain Machine Learning models and build AI Products in production. This role involves performing performance evaluations of LLM models and implementing LLMOps processes for the end-to-end lifecycle of LLMs. The ideal candidate will ensure high code quality, performance, and reliability through rigorous testing and adherence to best practices. They will demonstrate strong communication skills to articulate complex technical concepts to diverse stakeholders and collaborate effectively with cross-functional teams from Business and Technology domains to align project goals, gather requirements, and drive successful project outcomes.

Qualifications include a Masters or Bachelor's degree in Computer Science, Computer Engineering, Machine Learning, Data Mining, Information Systems, or related disciplines, with technical expertise in these areas or equivalent practical experience. A deep understanding of distributed systems, cloud infrastructure, and ML operations is essential. Solid knowledge of machine learning concepts, algorithms, and techniques, including developing and deploying ML models, is required. Expertise in multiple programming/scripting languages such as Python, Java, Scala, SQL, and NoSQL (e.g., HBase, Redis, Aerospike) is necessary. Proven experience with distributed systems, data streaming, complex event processing, and NoSQL solutions for data integration pipelines (batch and real-time) is expected. Experience with cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes) is required; Azure experience is a plus. A good understanding of machine learning libraries/frameworks like TensorFlow, PyTorch, and scikit-learn, along with expertise in Big Data technologies (Hadoop, Spark, HBase, Kafka) is beneficial. Prior experience in Content Understanding, enrichment, entity resolution, or knowledge graphs is advantageous. Experience developing Gen AI applications/services for sophisticated business use cases and large amounts of unstructured data, coupled with a strong background in MLOps and experimentation frameworks, is highly desirable.