Platform Integration-Deputy Director

Location: Hyderabad, Telangana, India

Overview Lead the SAP Integration, Data & AI (D&AI), and LLM Observability strategy, enabling seamless integration across all Observability platforms, Event Management, AIOps, and enterprise monitoring ecosystems. Drive the architecture and delivery of SAP and non-SAP integrations, ensuring unified telemetry, event correlation, intelligent automation, and AI-driven operational insights. establish governance and standards, and accelerate adoption of GenAI, LLM Observability, and Agentic AI capabilities to improve operational visibility, resilience, and business outcomes. Responsibilities Architect and lead enterprise-scale ITOM Health, Event Management, and Observability solutions aligned to business and technology objectives. Define and lead SAP platform integration with ITOM and enterprise observability platforms, enabling end-to-end service visibility and operational intelligence. Architect SAP and non-SAP integrations across , observability platforms, enterprise applications, and infrastructure ecosystems. Drive event management and intelligent event correlation capabilities across D&AI platforms to improve signal quality, service health, and operational response. Design scalable event ingestion and monitoring integrations using SNMP, REST APIs, MID Servers, syslog, webhooks, and streaming interfaces. Define enterprise strategies for event normalization, de-duplication, correlation, suppression, prioritization, and enrichment to reduce alert noise and improve actionable intelligence. Build and govern integrated observability frameworks spanning AppDynamics, Grafana, SolarWinds, Elastic, Splunk, and third-party monitoring platforms. Lead CMDB governance, CI lifecycle management, relationship accuracy, service mapping, and CSDM alignment to establish a reliable service topology foundation. Design and implement service health dashboards, operational KPIs, executive reporting, and business-aligned observability metrics. Enable automated incident creation, enrichment, triage, orchestration, and remediation workflows using Workflow, IntegrationHub, APIs, and automation platforms. Drive intelligent automation for observability use cases, including event-driven remediation, operational workflows, and autonomous response capabilities. Architect LLM Observability and AI Agent Observability capabilities covering model performance, agent behavior, telemetry, reliability, traceability, and operational health. Enable AI/GenAI-driven observability use cases for intelligent alerting, contextual insights, automated triage, root-cause analysis, anomaly detection, and predictive operations. Correlate logs, events, metrics, traces, topology, and service context to identify probable root causes and determine the business/service impact. Optimize MTTD and MTTR through proactive monitoring, intelligent event correlation, AI-driven insights, and automated remediation. Provide technical leadership, architecture governance, design standards, and strategic direction across ITOM, observability, integration, and automation initiatives. Conduct solution assessments, architecture reviews, performance optimization, and technology rationalization to improve scalability, reliability, and operational efficiency. Establish reusable enterprise integration and observability patterns that accelerate onboarding of applications, infrastructure, SAP services, and third-party platforms. Drive continuous evolution toward AIOps, Agentic Operations, and autonomous IT operations, leveraging AI agents and intelligent automation to transform traditional monitoring and incident management. Qualifications Candidate should have 12-15 Years of experience Bachelor's degree in Computer Science, Engineering, IT, or a related technical field. Hands-on expertise in ITOM, Event Management, Discovery, CMDB, Service Mapping, AIOps, and CSDM, with strong infrastructure, cloud, database, networking, and observability knowledge. Experienced in SAP– ITOM integration and D&AI platform integrations, connecting enterprise monitoring and observability ecosystems. Skilled in JavaScript, REST/SOAP APIs, scripting, AI Agents, workflow automation, agentic automation, and self-healing. Strong understanding of ITIL, SRE, Agile delivery, enterprise implementations, and stakeholder management. Experienced in LLM/AI-driven observability, identifying telemetry gaps and assessing business impact through Service Mapping and dependency intelligence.

Responsibilities

Architect and lead enterprise-scale ITOM Health, Event Management, and Observability solutions aligned to business and technology objectives. Define and lead SAP platform integration with ITOM and enterprise observability platforms, enabling end-to-end service visibility and operational intelligence. Architect SAP and non-SAP integrations across , observability platforms, enterprise applications, and infrastructure ecosystems. Drive event management and intelligent event correlation capabilities across D&AI platforms to improve signal quality, service health, and operational response. Design scalable event ingestion and monitoring integrations using SNMP, REST APIs, MID Servers, syslog, webhooks, and streaming interfaces. Define enterprise strategies for event normalization, de-duplication, correlation, suppression, prioritization, and enrichment to reduce alert noise and improve actionable intelligence. Build and govern integrated observability frameworks spanning AppDynamics, Grafana, SolarWinds, Elastic, Splunk, and third-party monitoring platforms. Lead CMDB governance, CI lifecycle management, relationship accuracy, service mapping, and CSDM alignment to establish a reliable service topology foundation. Design and implement service health dashboards, operational KPIs, executive reporting, and business-aligned observability metrics. Enable automated incident creation, enrichment, triage, orchestration, and remediation workflows using Workflow, IntegrationHub, APIs, and automation platforms. Drive intelligent automation for observability use cases, including event-driven remediation, operational workflows, and autonomous response capabilities. Architect LLM Observability and AI Agent Observability capabilities covering model performance, agent behavior, telemetry, reliability, traceability, and operational health. Enable AI/GenAI-driven observability use cases for intelligent alerting, contextual insights, automated triage, root-cause analysis, anomaly detection, and predictive operations. Correlate logs, events, metrics, traces, topology, and service context to identify probable root causes and determine the business/service impact. Optimize MTTD and MTTR through proactive monitoring, intelligent event correlation, AI-driven insights, and automated remediation. Provide technical leadership, architecture governance, design standards, and strategic direction across ITOM, observability, integration, and automation initiatives. Conduct solution assessments, architecture reviews, performance optimization, and technology rationalization to improve scalability, reliability, and operational efficiency. Establish reusable enterprise integration and observability patterns that accelerate onboarding of applications, infrastructure, SAP services, and third-party platforms. Drive continuous evolution toward AIOps, Agentic Operations, and autonomous IT operations, leveraging AI agents and intelligent automation to transform traditional monitoring and incident management.

Qualifications

Candidate should have 12-15 Years of experience Bachelor's degree in Computer Science, Engineering, IT, or a related technical field. Hands-on expertise in ITOM, Event Management, Discovery, CMDB, Service Mapping, AIOps, and CSDM, with strong infrastructure, cloud, database, networking, and observability knowledge. Experienced in SAP– ITOM integration and D&AI platform integrations, connecting enterprise monitoring and observability ecosystems. Skilled in JavaScript, REST/SOAP APIs, scripting, AI Agents, workflow automation, agentic automation, and self-healing. Strong understanding of ITIL, SRE, Agile delivery, enterprise implementations, and stakeholder management. Experienced in LLM/AI-driven observability, identifying telemetry gaps and assessing business impact through Service Mapping and dependency intelligence.