Software Engineer, AI Platform (FULLY REMOTE)-US Citizenship Required

As a Software Engineer, AI Platform at Splunk, your primary responsibilities will include designing, developing, and maintaining AI services that power our AI products. You'll implement and optimize CI/CD pipelines for seamless deployment of machine learning models. Additionally, you will manage and architect our cloud infrastructure using Infrastructure as Code principles. Collaboration is key; you'll work closely with ML Engineers and Applied Scientists to build efficient model serving systems while also monitoring system performance for scalability and operational excellence.

To qualify for the Software Engineer, AI Platform position at Splunk, you must be a US Citizen and hold a Bachelor's degree in Computer Science, Software Engineering, or a related field. A minimum of 3 years of software development experience using languages like Python and Golang is essential, along with strong cloud platform expertise (AWS, GCP, or Azure). Proficiency in containerization technologies such as Docker and Kubernetes, as well as experience with CI/CD tools, is also required. Knowledge of data storage technologies, RESTful APIs, and monitoring tools will greatly enhance your candidacy.

While it's not mandatory to have extensive experience in machine learning, having a foundational understanding and familiarity with machine learning frameworks like TensorFlow or PyTorch is highly beneficial for a Software Engineer, AI Platform at Splunk. This understanding will not only help you work effectively with our Applied Scientists but will also enhance your ability to contribute to the development and optimization of AI services and model serving systems.

In the role of Software Engineer, AI Platform at Splunk, you will engage with a variety of modern tools and technologies. You'll work extensively with cloud platforms like AWS or GCP and containerization technologies such as Docker and Kubernetes. Familiarity with CI/CD tools (like Jenkins or GitLab CI) is essential for optimizing the machine learning model deployment process. Additionally, knowledge of Infrastructure as Code tools like Terraform will play a significant role in managing and architecting our cloud infrastructure.

Alongside technical skills, the Software Engineer, AI Platform position at Splunk requires strong soft skills. You should possess excellent problem-solving abilities and analytical thinking to tackle complex challenges. Effective written and verbal communication is essential for collaboration with colleagues. The ability to work independently and in a team, coupled with a proactive approach to identifying and addressing problems, will greatly contribute to your success in this role.

In answering this question, provide specific examples of projects where you've utilized cloud platforms like AWS or GCP. Discuss the services you employed, the objectives you achieved, and how your experience aligns with Splunk's focus on robust cloud infrastructure management for AI services.

To effectively answer this question, share specific strategies you've implemented to streamline CI/CD processes. Talk about tools you've used, any challenges you faced, and how your optimizations impacted deployment speed and reliability, particularly related to machine learning model deployments.

When tackling this question, use the STAR method (Situation, Task, Action, Result) to structure your answer. Illustrate a specific challenge, outline the steps you took to address it, and emphasize the positive outcome that resulted from your actions, demonstrating your problem-solving skills relevant to the Software Engineer role at Splunk.

To answer this effectively, discuss your approaches to designing scalable systems, such as leveraging cloud services and implementing efficient algorithms. Share examples of how you've monitored system performance and upgraded infrastructure to meet increasing demand, showcasing relevant technical knowledge for the Software Engineer position at Splunk.

Discuss your familiarity with containerization and orchestration in your response. Share instances where you've used Docker to create reproducible environments and Kubernetes for managing those containers at scale. Emphasize how these experiences are relevant to the Software Engineer, AI Platform role at Splunk.

When answering this question, highlight your ability to collaborate with cross-functional teams. Detail a machine learning project, your specific role within the team, and how your contributions helped achieve the project's goals while emphasizing effective communication and teamwork, key aspects for the Software Engineer at Splunk.

In your response, define Infrastructure as Code (IaC) and discuss its significance in managing cloud infrastructure efficiently. Provide examples of tools you've used, such as Terraform, to automate deployment and provisioning, and explain how this practice supports the Software Engineer role at Splunk.

Discuss specific monitoring and observability tools you've utilized, like Prometheus or Grafana. Explain how these tools help track system performance metrics and contribute to making informed decisions regarding improvements and scalability, which are vital for a Software Engineer at Splunk.

In your response, share your approach to continuous learning, whether through online courses, webinars, reading industry publications, or participation in tech communities. Emphasize the importance of staying updated to maintain relevance in your role as a Software Engineer, AI Platform at Splunk.

To address this question, outline specific security practices you've implemented, such as regular audits, access management, or using security tools and frameworks. Companies like Splunk prioritize security, so showcasing your knowledge of best practices will resonate well in your interview for the Software Engineer role.