Application Tech Support Practitioner (Karnataka)

Project Role :Application Tech Support Practitioner

Project Role Description :Act as the ongoing interface between the client and the system or application. Dedicated to quality, using exceptional communication skills to keep our world class systems running. Can accurately define a client issue and can interpret and design a resolution based on deep product knowledge.

Must have skills :Enterprise Systems Monitoring Tools

Good to have skills :Dynatrace Administration Minimum 12 year(s) of experience is required

Educational Qualification :15 years full time education

Summary: Primary Skill:Observability & AIOps Secondary Skill:GenAI / Agentic AI for Operations

Role Summary

Responsible for leading the design, implementation, and operation of enterprise observability platforms, with a strong focus on AI driven, observability led operations. Accountable for enabling reliable, scalable, and intelligent Run operations through observability, automation, AIOps, and agentic AI capabilities across client and enterprise environments. Acts as a technical lead and force multiplier, enabling delivery teams to reduce noise, improve MTTR, and embed AI enabled operational workflows at scale.

Key Responsibilities Resource needs to be AI Ready Observability Platform Leadership Lead end to end observability design and implementation across metrics, logs, traces, events, synthetics, and user experience Provide hands on technical leadership across multiple enterprise observability platforms Define and standardize cross tool observability frameworks to reduce fragmentation Enable topology discovery, dependency mapping, and actionable dashboards for Ops, SRE, and leadership AI, AIOps & Agentic Operations Apply AIOps techniques including anomaly detection, event correlation, noise reduction, and predictive incident prevention Leverage GenAI for operational use cases, including: oIncident summarization oRoot cause analysis assistance oRunbook generation oKnowledge base enrichment Design and operationalize agentic AI workflows (diagnostic, orchestration, remediation agents) Enable semi automated and automated remediation through integrations and workflows Automation & Engineering Enablement Drive automation of deployments, health checks, upgrades, and routine operational tasks Implement self healing and auto remediation workflows using automation and CI/CD integration Embed observability and AI capabilities into cloud native and CI/CD architectures Collaboration & Delivery Enablement Partner with SRE, DevOps, Security, and Application teams to improve reliability and performance Act as an enablement lead, coaching teams on observability, AI, and agentic operations Translate technical signals into business relevant outcomes (stability, resilience, velocity)

Required Skills & Experience Observability & Tools 7+ years hands on experience across multiple (3+) observability platforms, such as: oDynatrace oDatadog oNew Relic oSplunk (Enterprise / ITSI / Observability Cloud) oPrometheus / Grafana Robust knowledge of: oMetrics, logs, traces, events oAlerting strategies and noise reduction oTopology and dependency mapping oITSM integrations (e.g. ServiceNow) AI / GenAI / Agentic AI Practical experience applying AIOps and ML for operations Working knowledge of: oLLM fundamentals oPrompt engineering for Ops use cases oAI assisted troubleshooting workflows Experience or strong exposure to agent based / agentic AI models, including orchestration concepts Cloud, Data & Automation Strong knowledge of AWS / Azure / GCP, containers, Kubernetes, and microservices Experience with telemetry pipelines, time series data, and streaming architectures Proficiency in Python and/or Java/Go for automation and integrations Experience with IaC and automation tools (Terraform, Ansible, Jenkins or equivalent) Professional Skills Strong client and stakeholder management capability Structured problem solving and critical thinking Ability to explain technical and AI concepts in business language Outcome driven mindset focused on MTTR, noise reduction, and operational resilience

Career Level Expectations CL7 Operates independently with accountability for complex, cross platform initiatives Acts as a technical authority within observability and AI enabled operations Influences operating models combining human + AI + agent based workflows Drives measurable outcomes rather than tool adoption alone

Success Measures Reduced alert noise and manual operational effort Improved MTTR and incident prevention through AI driven insights Observability embedded into Run workflows, not standalone tooling Delivery teams enabled to operate with reduced dependency on specialists Qualification15 years full time education Apply on Kit Job: kitjob.in/job/4n5uvr