Explore a career at the forefront of modern technology with Manager - AI Observability jobs. This pivotal leadership role sits at the intersection of artificial intelligence, software engineering, and IT operations, dedicated to ensuring the health, performance, and reliability of complex AI systems in production. As businesses increasingly rely on AI and machine learning models to drive decision-making and automate processes, the need for professionals who can manage and oversee the observability of these systems has never been greater. A Manager in AI Observability is responsible for building and leading a team of engineers to create a transparent, trustworthy, and efficient AI operational environment. Professionals in these roles typically oversee the strategy and implementation of a comprehensive observability framework. This framework is designed to provide deep insights into the behavior of AI models and the infrastructure they run on. Common responsibilities include defining the technical roadmap for monitoring, logging, tracing, and alerting systems specifically tailored for AI workloads. They manage the collection and analysis of key data points, such as model latency, throughput, prediction accuracy (data drift and concept drift), and resource utilization. A core part of the job involves translating this telemetry data into actionable intelligence, enabling proactive issue detection, rapid root cause analysis, and ensuring models perform as intended after deployment. This leadership position also involves cross-functional collaboration, working closely with data scientists, ML engineers, and product teams to establish Service Level Objectives (SLOs) and uphold a high standard of operational excellence. The typical skills and requirements for Manager - AI Observability jobs are a blend of technical depth and leadership acumen. A strong background in software engineering, DevOps, or Site Reliability Engineering (SRE) is fundamental, often coupled with experience in cloud platforms like AWS, GCP, or Azure. Candidates are expected to have hands-on knowledge of observability tools for metrics, logs, and traces (e.g., Prometheus, Grafana, ELK Stack, Jaeger) and understand how to apply them to machine learning pipelines. A solid grasp of MLOps principles and the machine learning lifecycle is crucial. Beyond technical expertise, successful managers possess exceptional leadership skills to mentor and grow a team, strong strategic thinking to align observability initiatives with business goals, and superb communication abilities to articulate complex technical concepts to non-technical stakeholders. They are results-oriented, adept at project management, and thrive in a dynamic environment where ensuring the reliability of AI is paramount. For those passionate about building resilient intelligent systems, Manager - AI Observability jobs offer a challenging and highly rewarding career path.
