Machine Learning Engineer

Experteer Overview

You will validate, deploy, and operationalize models in production while collaborating with cross-functional teams to improve system availability. This role offers a chance to work at the intersection of AI, HPC, and cyber‑security in a cutting-edge R&D environment.

• Design and develop ML/DL models to predict hardware failures and detect anomalies in HPC systems
• Apply time-series forecasting, anomaly detection, classification, and predictive maintenance on large-scale monitoring data
• Build and maintain data pipelines from infrastructure telemetry and logs
• Perform rigorous model validation for robustness and production readiness
• Deploy and operate models within a Kubernetes environment with scalable inference and lifecycle management
• Contribute to AI-driven cybersecurity use cases detecting abnormal behaviors or intrusions
• Work within an Agile/Scrum framework, participating in sprint activities
• Collaborate with system administrators, support teams, and data engineers to translate operational challenges into data-driven solutions

• Strong experience with ML and DL frameworks (TensorFlow, PyTorch, Scikit-learn)
• Experience with time-series data and anomaly detection
• Proficiency in Python and data science ecosystem
• Experience with Prometheus or similar monitoring/telemetry systems
• Familiarity with containerization/orchestration, especially Kubernetes
• Experience building production-grade ML pipelines
• Experience handling large-scale monitoring/operational datasets
• Understanding of distributed systems and infrastructure monitoring
• Knowledge of HPC environments, GPUs, and high-speed interconnects (e.g., Infiniband) is highly desirable
• Proficiency with Git-based version control (GitHub, GitLab)
• Linux proficiency
• Understanding of Scrum; experience with Jira and Confluence

• Flexible Work Schedule: Half day Fridays
• Learning and Growth: opportunities to work with advanced AI technologies