Knowledge Graph Snr. Senior System Development Engineer, Central Reliability Maintenance Engineering

BASIC QUALIFICATIONS
- Bachelor’s or Master’s degree in computer science, software engineering, computer engineering, or equivalent
- Experience with modern programming languages such as Python, C++, JavaScript, or Go including object-oriented design
- 5+ years of non-internship professional software development experience, including development life cycle, coding standards, code reviews, source control management, build processes, and testing
- 5+ years of non-internship experience designing architectures (design patterns, reliability, and scaling) of new and existing systems
- Experience automating, building, and testing system, including CI/CD pipelines build processes
PREFERRED QUALIFICATIONS
- Experience in systems development with a focus on large-scale data systems, knowledge graphs, or graph-based technologies
- Strong knowledge of data structures, algorithms, big "O" analysis and designing for performance, scalability and availability
- Strong understanding of graph data structures, algorithms, and graph database technologies
- Experience as a mentor, tech lead, or leading an engineering team
- Experience in other cloud computing platforms such as AWS
- Knowledge of a front-end technologies such as JavaScript, Rust, React, CSS, HTML, etc

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At Amazon we believe that Every Day is still Day One! We’re working to be the most customer-centric company on earth. Are you passionate about pushing the boundaries of semantic technologies and knowledge graphs? Do you want to shape the future of data-driven decision-making at one of the world's most innovative companies?

At Amazon's Central Reliability Maintenance Engineering team (C-RME), we are looking for a talented Senior System Development Engineer to fast-track our next-generation of graph-based AI systems that integrate all services and insights important to deliver World-Class, AI-augmented maintenance practices.

As a Sr. System Development Engineer in Decision Science and Technology, you play a critical role in advancing AI technologies that enable our internal customers to leverage maintenance and equipment health data stored in our RME knowledge graph. You will work with a wide range of partner teams and will be a key contributor to the design and implementation of new AI solutions as well as overall cross-functional systems integrations. You will work with internal RME customers (>35,000 customers worldwide) to develop requirements for new and current Knowledge Experience and Technology (KxT) solutions. You will work closely with the team’s Senior Scientists and Knowledge Graph Engineer to propose, architect, develop and deploy state-of-the-art systems that process data and facts from structured, semi structured, and unstructured knowledge sources in real-time. Your daily activities will range from strategic planning, architecture design, new systems development to supporting the customers that leverage your deliverables.

You will work closely with the team’s Scientists and Knowledge Graph Engineer to propose, architect, develop and deploy state-of-the art systems that process data and facts from structured, semi structured and unstructured knowledge sources in real-time. Your daily activities will range from strategic planning, architecture design, new systems development to supporting the customers that leverage your deliverables.

Key job responsibilities
As a Senior Systems Development Engineer, you will work backwards from our customer experience to design, implement, test, deploy, and maintain high quality, highly available, innovative graph-based solutions that meet business requirements and transform org-wide decision-making capabilities. You will:
• Lead the design, implementation and successful delivery of graph-based and neuro-symbolic AI solutions addressing complex customer problems, by developing new softwares, systems, infrastructure, or refactor existing / legacy products;
• Translate complex functional and technical requirements into detailed architecture and design documentation and lead reviews of architecture, design, operations, processes and post-incident analysis for Knowledge Experience and Technology team;
• Work closely with our Senior Knowledge Graph engineer to design and implement high-throughput, cost-effective knowledge pipelines to extract, transform and load data, and information from knowledge sources of different types and format;
• Collaborate with our Scientists on the design and optimization of graph traversal, query, and indexing approaches to ensure faster data retrieval and scalability of our solutions;
• Contribute to develop the team’s technical strategy by closely working with and influencing other teams, Technical Program Managers or Product Managers;
• Drive automation initiatives and refactoring of existing solutions, including incorporating extractive and / or generative AI as well as machine learning (ML) components where they bring the most value;
• Deliver technical solutions which improve engineering and operational processes within the team as well as reduce complexity, and work to apply those improvements within our team and with partner teams to enable greater agility and deliver faster on behalf of our customers; and
• Report and analyze usage patterns of the tools and services that you deliver.

About the team
The Amazon Reliability and Maintenance Engineering (RME) team maintains and optimizes technologies ranging from large, modern, purpose-built warehouses utilizing robotics and high-volume conveyance all the way through the value chain to small, high-speed warehouses placed as close to our customers as possible. Central Reliability Maintenance Engineering (RME) uses science and data to drive scalable maintenance best practices across Amazon business units globally. We do this to meet our customer promise, reduce costs, and support the Climate Pledge. The Decision Science & Technology (DST) team within Reliability Maintenance Engineering (RME) specializes in advanced analytics and artificial intelligence solutions. The team uses machine learning to develop predictive models for spare parts, cycle-based maintenance, predictive maintenance, energy consumption, and refrigeration health status monitoring programs. We also leverage knowledge representation and reasoning approaches to support troubleshooting tools, accelerate root cause analyses, and enhance knowledge management systems.