Senior Manager, Process & AI Automation, Application Reliability

Johnson & Johnson•Raritan, NJ

19d•Hybrid

About The Position

We are seeking a multifaceted and innovative Senior Manager, Process & AI Automation, Application Reliability to spearhead our efforts in enhancing application reliability through innovative technologies. This role involves designing and implementing strategies around observability, AI, and automation to optimize operational efficiency, predictability, and customer satisfaction. The ideal candidate will be a leader with vision in integrating AI-driven insights into our processes and driving a cultural shift toward intelligent automation. The role will located in Raritan, NJ. This is an exciting opportunity to shape the future of our global, cross-sector service. You’ll work hands-on with Application Reliability service managers, ITSM process owners, process users and platform engineers to uncover automation opportunities, design intelligent workflows, and bring operational efficiencies to life through innovative technology. What we're looking for: A curious mind with a passion for improving operations and user experience through automation Someone who sees the big picture but sweats the details of process design and data flow. An experienced process engineer and workflow optimizer A natural collaborator who enjoys engaging with team members and turning ideas into working solutions. A data-driven decision maker A self-starter and tech enthusiast who’s not afraid to experiment, iterate, reinvent and lead change.

Requirements

Bachelor's Degree
Solid understanding of core ITSM processes: Incident, Problem, Change, and Request Management
Expertise in mapping and re-engineering processes and user journeys for automation suitability, including experience with process mining tools or BPMN
Technical Expertise: Demonstrable experience with application support, reliability engineering, or DevOps in complex environments.
Hands-on experience crafting and building automations in ServiceNow (Flow Designer, Business Rules, Workflows, etc.) or other workflow-based platforms.
Advanced scripting skills (Python, PowerShell, Go) for automation and integrations.
Strong knowledge of observability tools (e.g., Grafana, AppDynamics, Splunk) and AIOps platforms (e.g., Moogsoft, BigPanda, Splunk ITSI, Dynatrace Davis).
Hands-on experience with AI/ML techniques and frameworks (e.g., Python, TensorFlow, scikit-learn), including deploying models in production IT environments.
Familiarity with automation tools (e.g., Ansible, Puppet, Jenkins, Terraform) and cloud-native automation (AWS, Azure, GCP).
Solid skills in SQL and data visualization (Power BI, Tableau) for telemetry and operational analytics.
Experience with system integrations across diverse platforms.
Strategic & Analytical Thinking: Ability to design and articulate strategic plans for integrating AI and observability into operational workflows.
Strong analytical skills with the ability to interpret large data sets and derive actionable insights.
Leadership & Collaboration: Demonstrable experience in multi-functional teams or projects with a focus on digital transformation.
Ability to collaborate multi-functionally and translate partner needs into practical automation designs.
Excellent communication skills to translate technical concepts into business value.

Nice To Haves

ServiceNow certifications
ITIL 3/4 certification
IT Operations experience (application / infrastructure)

Responsibilities

Own the development and execution of strategies using AI, observability tools, and automation to improve application reliability and support functions.
Deploy self-healing, auto-remediation, and automated incident response mechanisms.
Define and track critical metrics for automation impact and reliability improvements.
Report automation value and reliability metrics to senior leadership.
Collaborate with multi-functional teams to identify automation opportunities and implement scalable solutions to streamline incident management, troubleshooting, and system monitoring.
Drive the adoption of observability platforms and analytics to proactively determine and resolve potential issues before impacting end-users.
Develop innovative methodologies using AI/ML models for predictive maintenance, anomaly detection, and root cause analysis.
Establish guidelines for monitoring, alerting, and incident response workflows grounded in automation and AI insights.
Design and document automated workflows, exception handling, and escalation paths.
Lead the evaluation, recommendation, and integration of emerging technologies to enhance operational capabilities.
Promote a data-driven culture with continuous improvement initiatives based on insights derived from observability and AI tools.