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AI Platform Adoption Engineer - Run

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Hybrid

AI Platform Adoption Engineer - Run

Cognizant

AI Platform Adoption Engineer - Run

Skills

AINo-code Automations

15 days ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Hybrid

Published on:

12 October 2026

What they need


Role & responsibilities

Key Responsibilities

  • Customer Discovery, Adoption & Operationalization: Lead discovery and onboarding workshops; assess infrastructure, applications, cloud, observability, ITSM, incident, problem, change, and support processes; define maturity baseline, target operating model, use cases, rollout roadmap, adoption plan, service ownership, support boundaries, escalation paths, runbooks, production-readiness criteria, and transition-to-run controls.
  • Observability & Operational Intelligence: Enable end-to-end operational visibility by integrating metrics, logs, traces, events, alerts, topology, service health, and operational context; operationalize event intelligence, alert prioritization, incident prediction, AI-assisted triage, RCA, remediation recommendations, operational copilots, and agent-assisted decision-making.
  • Workflow Automation & Self-Healing: Design and implement intelligent routing, ticket enrichment, notifications, approvals, escalations, handoffs, closures, runbook automation, and self-healing workflows; define guardrails, approval mechanisms, risk tiers, rollback controls, and effectiveness measures for automated remediation.
  • Operational Knowledge & AI Enablement: Curate and govern runbooks, SOPs, knowledge articles, CMDB data, service maps, architecture documentation, incident history, known-error records, and resolution evidence to power agents, workflows, and AI-driven operations.
  • Governance & Value Realization: Establish access controls, human-in-the-loop checkpoints, auditability, traceability, data-handling policies, and governance for AI recommendations and actions; track adoption, automation coverage, ticket deflection, alert-noise reduction, self-healing success, MTTA, MTTR, incident recurrence, service availability, operational efficiency, and cost outcomes.
  • Stakeholder Management & BAU Transition: Serve as the primary interface between customer operations teams and AI Platforms, engineering, and delivery teams; lead adoption reviews, remove onboarding barriers, enable self-service, transfer knowledge, and transition governed BAU ownership to customer teams.

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