Lead Site Reliability Engineer

Why GMF Technology?

Innovation isn’t just a talking point at GM Financial, it’s how we operate. From generative AI and cloud-native technologies to peer-led learning and hackathons, our tech teams are building real solutions that make a difference. We’re committed to AI-powered transformation, using advanced machine learning and automation to help us reimagine customer interactions and modernize operations, positioning GM Financial as a leader in digital innovation within a dynamic industry.

Join us and discover a workplace where your ideas matter, your development is prioritized, and you can truly make a global impact.

About the Role:

We are expanding our efforts into complementary data technologies for decision support in areas of ingesting and processing large data sets. Our interests are in enabling data science and search based applications on large and low latent data sets in both a batch and streaming context for processing. To that end, this role will incorporates aspects of software engineering and operations, combining SRE and DevOps skills to come up with efficient ways of managing and operating applications. The role will require a high level of responsibility and accountability to deliver technical solutions. The data sets we deal with support both off-line and in-line machine learning training and model execution. Other data sets support search engine based analytics. Exploration and deployment of technologies activities include identifying opportunities that impact business strategy, selecting data solutions software, and defining hardware requirements based on business requirements. Responsibility also includes documentation of procedures for deployment, monitoring, managing and switching the environments in production and disaster recovery sites. This role participates along with team counterparts to architect an end-to-end framework developed on a group of core data technologies

• Manage/Administer/Deploy Kubernetes and Spark cluster environments, on bare-metal and container infrastructure, including service allocation and configuration for the cluster, capacity planning, performance tuning, and ongoing monitoring
• Define and refine processes and procedures for the site reliability engineering practice
• Setup, manage and maintain Kubernetes based scalable environments for high-availability and work with vendors for smooth and continuous operations
• Work closely with data scientists, data architects, data engineers, ETL developers, cybersecurity, network, Linux, other IT counterparts, and business partners to design and setup the environments to manage the ingested and processed datasets from the external sources, internal systems, and the data warehouse to extract features of interest
• Evaluate, research, experiment with data processing, management and scalability technologies in a lab to keep pace with industry innovation while assessing business impact and viability for use cases associated with efforts in hand
• Design, setup, test, deploy, monitor, document, and troubleshoot data processing and associated automation issues from the operations perspective
• Work with IT Operations and Information Security Operations with monitoring and troubleshooting of incidents to maintain service levels
• Work with Information Security Vulnerability Management and vendors to remediate known impacting vulnerabilities
• Contribute to the evolving distributed systems architecture to meet changing requirements for scaling, reliability, performance, manageability, and cost
• Report utilization and performance metrics to user communities
• Contribute to planning and implementation of new/upgraded hardware and software releases
• Responsible for monitoring the Linux, Kubernetes, Object Storage(MinIO), Feature Store, and Spark
• Research and recommend innovative, and where possible, automated approaches for administration tasks
• Identify approaches to efficiencies in resource utilization, provide economies of scale, and simplify support issues
• Responsible for administration of Machine Learning platforms & Operations (MLOps) Such as Kubeflow/Jupyterhub/Python
• This role will support GMF international operations and will closely align with our GMF IT NorthStar architecture and operating Principles