AI Operations Tech Leader

Job Summary:

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.

 

Key Responsibilities

Hands-on Data & Al Solutions for Operations Support

•            Actively lead and contribute to high-impact data/AI projects that directly improve operations support outcomes — e.g., real-time incident enrichment, predictive alerting, automated root-cause analysis, change risk scoring, ticket clustering & autotriage, knowledge mining for support agents, and intelligent runbooks.

•            Design and deliver scalable features embedded into operations support workflows and platforms (ServiceNow, Jira Service Management, monitoring tools, ITSM systems, etc.) in collaboration with multidisciplinary competency teams.

•            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

AIOps Tools & Platform Leadership for Operations Support

•            Lead the architecture, development, and continuous enhancement of internal AIOps platforms and reusable components that power operations support teams — including integration with ITSM, observability (Prometheus/Grafana/ELK/Dynatrace/Splunk), ticketing, and automation tooling.

•            Support  MLOps/AlOps best practices specifically for production operations support Al systems: model monitoring in live ops environments, drift & performance degradation detection, rollback mechanisms, and cost control at operational scale.

Al Technical Leadership for Operations Support Initiatives

•            Serve as the lead Al technical authority and trusted advisor for all operations support programs, automation movements, and Al transformation efforts across service operations, NOC, support desks, infrastructure operations, and reliability engineering.

•            Lead technical discussions, architecture reviews, PoCs, vendor evaluations, and solution selection whenever Al is being considered or applied to operations support challenges.

•            Identify, prioritize, and drive the highest-ROI Al use cases in operations support —

e.g., reducing MTTR/MTTD, automating Level 1 triage, predicting PI incidents, autogenerating post-mortems, optimizing shift handovers, and enabling proactive operations support.

 

 

Team & People Leadership

•            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 (reduced toil, faster resolution, higher availability).

•            Perform technical coaching, design/code reviews, and career development with emphasis on operations support domain knowledge.

Stakeholder & Cross-Functional Collaboration

•            Partner intensively with operations support leaders, incident managers, service owners, reliability engineers, ITSM/process teams, and infrastructure groups to align Al initiatives with operational priorities and pain points.

•            Strong collaboration with DS&AI Competency.

 

Qualifications & Experience

Required

•            10+ years in data engineering, Al/ML engineering, or operations support technology roles, with 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 (MTT R, MTT D, ticket deflection, toil reduction, availability).

•            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 (ServiceNow, PagerDuty, Splunk, Datadog, Moogsoft, BigPanda, etc.)., Databricks, Azure/ADF.

 

•            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/internaI tooling platforms specifically for operations support teams.

 

 

Preferred

•            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.

Leadership & Soft Skills

•            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.