Lead AIOps Engineer

We are looking for a Lead AIOps Engineer to help build and scale AI-driven operations across the full service management lifecycle. This is a hands-on role for someone who can turn operational signals and service management data into actionable intelligence and automation that improves reliability, reduces manual effort, accelerates recovery, and increases delivery velocity. You will lead the engineering work to deliver production-grade capabilities such as event correlation, anomaly detection, predictive monitoring, automated remediation, and intelligent workflow automation. The value of AIOps is highest when it reduces noise, surfaces actionable insights earlier, and connects those insights to automated actions across incident, change, release, and request workflows. If you are passionate about technology, thrive on learning, and want to build practical AI systems that create measurable operational and delivery outcomes, this role is for you. About the Role In this opportunity as a Lead AIOps Engineer, you will: Build and deploy AIOps capabilities that reduce alert fatigue and improve incident response, including event correlation and noise reduction techniques. Develop and productionize machine learning approaches for operational use cases such as anomaly detection, predictive monitoring, and early-warning signals from telemetry and operational data. Design and implement data pipelines that ingest, normalize, and enrich high-volume operational signals and service management data (for example: monitoring signals combined with tickets, changes, releases, and operational knowledge) to enable intelligent automation. Create and maintain automation (scripts, runbooks, workflows) that turns insights into action and enables automated incident response and remediation, with appropriate safeguards and validation steps. Engineer intelligent, AI-native service management workflows that reduce friction and cycle time, such as smarter routing, enrichment, summarization, and decision support across incident, change, and release workflows. Partner with engineering and platform teams to automate release and change activities where possible, including automated change creation, deployment signal ingestion, release health checks, post-deployment verification, and rollback decision support, to accelerate time-to-market while reducing risk. Establish engineering standards for model performance, explainability, and safe automation, including feedback loops to reduce false positives and improve trust over time. Mentor and guide other engineers through technical leadership, code reviews, patterns, and reusable components. About You You’re a fit for the role of Lead AIOps Engineer if you have the following required qualifications: Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related field, or equivalent practical experience. 8+ years of experience in software engineering, ML engineering, data engineering, DevOps/SRE, platform engineering, or automation-heavy operations roles. At least 3 years of hands-on AI/ML development and engineering experience, including building and training models and deploying them into production or production-like environments. Strong coding ability (Python plus other languages used for services and automation) and experience building reliable, maintainable production systems. Experience working with operational telemetry such as metrics, logs, events, and traces, and using those signals to drive detection and response. Experience connecting operational intelligence to workflow execution through automation or orchestration, with a bias toward simplifying processes and accelerating delivery. Proven ability to translate ambiguous problems into shipped solutions that improve operational and delivery outcomes. A genuine passion for technology and continuous learning, with a track record of staying current as tools and techniques evolve. Nice to have Experience with AIOps, event intelligence, or observability platforms focused on correlation and actionable incident reduction. Familiarity with ML frameworks and model lifecycle practices such as monitoring, retraining, and evaluation. Experience with cloud platforms and modern infrastructure patterns including containers, Kubernetes, and infrastructure as code. Experience integrating AI-driven insights with service management workflows, including change and release automation patterns. Why this role matters Modern environments generate more operational and service management data than humans can manually interpret in real time. AIOps helps teams detect anomalies, correlate signals, predict issues, and reduce noise so engineers can focus on what matters. In this role, you will build systems that improve reliability and customer experience while also improving delivery speed by making service management workflows more intelligent, automated, and AI-native. #LI-AB3 What’s in it For You? Hybrid Work Model: We’ve adopted a flexible hybrid working environment (2-3 days a week in the office depending on the role) for our office-based roles while delivering a seamless experience that is digitally and physically connected. Flexibility & Work-Life Balance: Flex My Way is a set of supportive workplace policies designed to help manage personal and professional responsibilities, whether caring for family, giving back to the community, or finding time to refresh and reset. This builds upon our flexible work arrangements, including work from anywhere for up to 8 weeks per year, empowering employees to achieve a better work-life balance. Career Development and Growth: By fostering a culture of continuous learning and skill development, we prepare our talent to tackle tomorrow’s challenges and deliver real-world solutions. Our Grow My Way programming and skills-first approach ensures you have the tools and knowledge to grow, lead, and thrive i