AI Authoring/Monitoring SRE Eng.

Job Description
As an AI Authoring/Monitoring SRE, you will ensure the reliability, scalability, and efficiency of AI authoring systems and monitoring tools. You will bridge the gap between development and operations, working on both proactive system improvements and reactive incident resolution. This role requires hands-on experience in building, monitoring, and maintaining AI-driven applications and services, emphasizing reliability and operational excellence. Typical Responsibilities ensuring the stability and reliability of AI systems by monitoring model performance, troubleshooting issues, and optimizing infrastructure work closely with development teams to deploy updates, maintain continuous monitoring frameworks, and ensure that AI systems scale effectively while meeting operational and compliance standards. Candidate Requirements Technical Skills: Strong proficiency in scripting and programming languages like Python, Go, or Bash. Solid experience with monitoring tools (e.g., Prometheus, Grafana, ELK Stack, Datadog). Expertise in Linux/Unix systems and system administration. Hands-on experience with cloud platforms (AWS, Azure, GCP) and their AI-related services. Familiarity with containerization (Docker) and orchestration (Kubernetes) technologies. Proficient in version control systems (e.g., Git) and CI/CD tools (e.g., Jenkins, GitLab CI). Understanding of machine learning pipelines, data preprocessing, and deployment workflows. Education and Experience: Bachelor degree in Computer Science, Information Technology, or a related field. Five years of experience in SRE, DevOps, or related roles, with exposure to AI/ML systems. Soft Skills: Strong problem-solving and analytical thinking abilities. Excellent communication skills to collaborate across multidisciplinary teams. Ability to work in a fast-paced, agile environment with minimal supervision. Nice-to-Have: Knowledge of AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Experience with big data tools like Apache Spark, Kafka, or Hadoop. Familiarity with configuration management tools (e.g., Ansible, Terraform). Exposure to AI observability and debugging practices.