Senior Backend Java ML Engineer

July 24

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Rackspace Technology

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IT as a Service • Multi-Cloud • Managed Hosting • Managed AWS/Azure/Google Cloud Platform/OpenStack/Alibaba • Managed Private Cloud for VMware/Microsoft/OpenStack

5001 - 10000

Description

• Collaborate and Integrate: Work closely with the Research team and Product Managers to understand requirements and translate them into robust backend solutions. Ensure seamless integration of machine learning models into production environments. • Design and Development: Architect, design, and develop distributed, scalable, and high-performance software solutions. Apply best practices in software engineering to ensure code quality, reliability, and maintainability. • Proactive Issue Resolution: Take initiative to identify potential issues and proactively resolve them. Ensure the robustness and reliability of the backend systems through continuous monitoring and improvement. • Operability Measures: Implement and maintain operability measures such as logging, monitoring, alerting, and debugging. Ensure that systems are always running smoothly and any issues are quickly addressed. • DevOps Practices: Embrace DevOps methodologies to streamline the development and deployment process. Utilize CI/CD pipelines and build tools like Maven and Gradle to ensure efficient and reliable software delivery. • Performance Optimization: Continuously analyze and optimize the performance of backend systems. Ensure that applications can handle high volumes of traffic and data efficiently. • Documentation and Best Practices: Create and maintain comprehensive documentation for the backend systems and processes. Share best practices with the team and contribute to a culture of continuous improvement.

Requirements

• Proficiency in Java: Demonstrable expertise in Java programming, with a deep understanding of its ecosystem and best practices. • Experience in Distributed Systems: Proven experience in designing, developing, and maintaining distributed, scalable, and high-performance software. Ability to handle complex system architectures. • Machine Learning and Big Data: Practical knowledge of machine learning algorithms and big data technologies. Experience with Hadoop, Oozie, Pig, Hive, HBase, Vertex AI and AutoML along with machine learning frameworks (TensorFlow, PyTorch) and libraries (e.g., scikit-learn). • Collaboration and Communication: Strong ability to work collaboratively with cross-functional teams, including researchers and product managers. Excellent communication skills to effectively convey technical concepts and ideas. • Proactivity: Ability to take initiative and be proactive in identifying and resolving issues. Strong problem-solving skills and a can-do attitude. • Operability Experience: Experience with implementing and maintaining operability measures such as logging, monitoring, alerting, and debugging. Ensuring system health and reliability. • DevOps Practices: Familiarity with DevOps practices, CI/CD pipelines, and build tools like Maven and Gradle. Experience with automation and continuous integration. • Performance Optimization: Ability to analyze and optimize system performance. Ensuring applications can handle high volumes of traffic and data efficiently. • Containerization: Knowledge of containerization technologies such as Kubernetes (K8S) and Docker. Experience with container orchestration and management. • GCP Vertex AI: Experience with Google Cloud Platform (GCP) Vertex AI. Ability to leverage cloud-based tools and services for machine learning and data processing.