Senior Backend Java ML Engineer- US Remote

Requirements:

• 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. 

Must Have:

• 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.