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We are looking for an experienced Al Ops Tech Leader — Operations Support to lead the intelligent transformation of our operations support functions. This senior technical leadership role combines deep hands-on contribution to data & Al solutions that directly enhance operations support processes with strategic leadership in developing AIOps tools/platforms and driving Al technical direction for all operations support initiatives. The role is explicitly centered on operations support domains — including incident management, major incident response, problem management, change enablement, service desk / Level 1—3 support, monitoring & observability, service reliability, and operational resilience. You will remain actively involved (-40—50%) in delivering technical support in data solutions that solve real operations support pain points, while leading the design, build, and evolution of AIOps tooling and serving as the principal Al technical authority for operations support transformation programs. This is a player-coach position in the operations support space: hands-on technical delivery + team leadership + Al architecture governance for operational excellence.
Job Responsibility:
Actively lead and contribute to high-impact data/AI projects that directly improve operations support outcomes
Design and deliver scalable features embedded into operations support workflows and platforms
Ensure solutions meet strict operations support SLAs for reliability, low latency, auditability, explainability, and zero-downtime deployment
Up-to-date with innovations and research in AIOPS Tools
Lead the architecture, development, and continuous enhancement of internal AIOps platforms and reusable components that power operations support teams
Serve as the lead Al technical authority and trusted advisor for all operations support programs, automation movements, and Al transformation efforts
Lead technical discussions, architecture reviews, PoCs, vendor evaluations, and solution selection
Identify, prioritize, and drive the highest-ROI Al use cases in operations support
Build, mentor, and lead a high-performing squad of AIOps specialists focused on operations support outcomes
Foster a culture of rapid experimentation, production-first mindset, and relentless focus on operational impact
Perform technical coaching, design/code reviews, and career development
Partner intensively with operations support leaders, incident managers, service owners, reliability engineers, ITSM/process teams, and infrastructure groups
Strong collaboration with DS&AI Competency
Requirements:
10+ years in data engineering, Al/ML engineering, or operations support technology roles
4—6+ years in technical leadership positions within operations support / IT operations / service operations environments
Proven track record delivering production Al/ML/data solutions that measurably improved operations support KPIs
Strong hands-on expertise with modern data/AI stacks (Python, Spark, Kafka, Airflow, cloud data platforms, PyTorch/TensorFlow, LLM frameworks) and integration into operations support ecosystems
Deep practical experience with AIOps patterns in live operations support settings: event correlation, anomaly detection, automated actions, predictive analytics, GenAI for ops
Experience leading development or significant enhancement of AIOps/internal tooling platforms specifically for operations support teams
Ability to stay deeply technical while leading people and strategy in a high-velocity operations support context
Excellent communication — can explain complex Al concepts to operations support practitioners and translate operational pain into technical roadmaps for executives
Strong bias for action, production impact, and reducing operational toil through intelligent automation
Nice to have:
Background in ITIL-aligned operations support processes (incident, problem, change, service request, knowledge management)
Hands-on work with GenAl/LLM applications in operations support (ops copilots, auto-remediation agents, intelligent knowledge search, summarization of alerts/incidents)
Prior success scaling AIOps capabilities in large-scale operations support / NOC / shared service environments