Machine Learning Engineer (MLE), Artificial Intelligence Join us as we pursue our disruptive new vision to make machine data accessible, usable and valuable to everyone.
We are a company filled with people who are passionate about our product and seek to deliver the best experience for our customers.
At Splunk, we’re committed to our work, customers, having fun and most importantly to each other’s success. Learn more about Splunk careers and how you can become a part of our journey!Role : As a Machine Learning Engineer in the Artificial Intelligence group, you will be responsible for developing the core AI / ML capabilities to power the entire Splunk product portfolio and help our customers to drive their journey to digital resiliency.
You will collaborate with cross-functional teams, mentor junior team members, and help drive the engineering roadmap of the area.
Responsibilities : The responsibilities of this role include :
- Development of the AI / ML platform and infrastructure that drives our product’s key ML use cases in the cybersecurity and observability domains.
- Collaborate closely with software engineers, applied scientists, and product managers to integrate generative AI solutions into our products and services.
- Stay up to date with the latest developments in the field of AI / ML, and ensure that these advancements are properly incorporated into our technology roadmap.
- Actively participate in cross-functional discussions and strategic decisions related to AI directions and product roadmaps.
Requirements : Knowledge, Skills, and Abilities :
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field with at least 3+ years of industry experience.
- Experience with containerization and orchestration tools (e.g., Docker, Kubernetes).
- Experience with model deployment and serving into production environments
- Knowledge of version control systems, especially Git.
- Knowledge of CI / CD principles and tools.
- Familiarity with cloud platforms (AWS, GCP, Azure) and serverless architecture.
- Experience with MLOps platforms such as MLflow or Kubeflow.
- Previous experience working in cross-functional teams and collaborating with data scientists and DevOps teams.
- Excellent problem-solving skills and the ability to troubleshoot complex issues.
- Excellent communication skills with the ability to articulate complex technical concepts to both technical and non-technical audiences.
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