Software Development Engineer

Mirantis is hiring a Software Development Engineer for its R&D team in the Office of the CTO. The role builds AI-driven testing, anomaly detection, forecasting, and root-cause analysis tools, and integrates them into engineering workflows and Kubernetes deployments.

Responsibilities:

• Build and maintain the web driver and test agents that run end-to-end scenarios against the k0rdent web UI and APIs, then turn what they find into clear, reproducible defects for the teams that own the code. A large part of the work is making test runs safe to point at live systems: keeping sessions and context across runs, cleaning up every change a run makes, and requiring a review step before anything is written to shared systems such as Jira. You will also evaluate language models for the harness with repeatable test scenarios, and recommend changes based on measured results
• Develop anomaly detection and capacity forecasting models, such as disk fill and memory leak prediction, using metrics and logs from running k0rdent environments. Measure detection quality against labeled failure sets and real workloads, and improve the models from the results. Build LLM-assisted root-cause analysis that turns a raw alert into a likely cause and a suggested fix, and work with the observability team to deliver confirmed findings into the platform's alerting and Grafana dashboards
• Help bring these tools into day-to-day engineering at Mirantis, so the test harness runs against new builds and pull requests and reports to the owning teams. Work with the KOF, QA and k0rdent engineering teams to find other places where AI models and data science can improve our software and tooling. Package and deploy what you build to Kubernetes with container images, Helm charts and CI pipelines that follow Mirantis release conventions

Requirements:

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics or a related field, or equivalent practical experience
• 0 to 2 years of professional software development experience, including internships
• Strong Python, including practical experience with at least one ML framework (scikit-learn, PyTorch, TensorFlow or Keras)
• Working proficiency in TypeScript
• Hands-on experience with at least one of: anomaly detection, time-series forecasting, or outlier detection
• Experience building LLM applications, such as agentic loops, tool use, prompt design or model evaluation
• Working knowledge of Docker, Git and CI pipelines
• Demonstrated ability to ship end-to-end projects, from data ingestion through a working interface or report
• Experience with Kubernetes and Helm
• Experience with observability stacks such as Prometheus, VictoriaMetrics, OpenTelemetry and Grafana
• Experience with browser automation or end-to-end test frameworks
• Familiarity with the Model Context Protocol (MCP), LLM gateways or multi-model routing
• Experience with graph-based or topology-aware root-cause analysis