Technology OpS Support Practitioner
IT Operations Manager
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Full time
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Experience: 10-12 years
ジョブ番号 ATCI-5469228-S1997311
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Bengaluru
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Required Skill: AI & Data Solution Architecture
Project Role : Technology OpS Support Practitioner
Project Role Description : Own the integrity and governance of systems, including best practices for delivering services. Develop, deploy and support infrastructures, applications and technology initiatives from an architectural and operational perspective in conjunction with existing standards and methods of delivery.
Must have skills : AI & Data Solution Architecture
Good to have skills : IT Infrastructure Operations, Value Engineering (VE)
Minimum 12 year(s) of experience is required
Educational Qualification : 15 years full time education
Role Summary
AI Powered Tech Talent
Leads the design and adoption of AI first Run Operations, applying GenAI and Agentic AI to real production problems such as incident reduction, noise elimination, and self healing.
This role is deeply grounded in enterprise production operations, combining observability led thinking, agentic orchestration, and ITSM aware governance to deliver measurable operational outcomes, not PoCs or dashboards.
Acts as a senior technical and operational authority, able to reason in ambiguity and operate effectively in complex, imperfect Run environments.
Key Responsibilities
AI First Run Operations
- Apply GenAI and Agentic AI to day to day Run problems, including incident reduction, alert noise elimination, and self healing
- Focus on production outcomes, not experimentation (MTTR reduction, ticket deflection, automation coverage)
- Design AI solutions that augment Run teams while preserving operational trust and accountability
Agentic Orchestration & Automation
- Decompose complex Run workflows (incident diagnosis remediation validation) into agent driven, orchestrated steps
- Design agent interactions across diagnostic, remediation, and validation stages
- Replace static runbooks with dynamic, agent orchestrated workflows
Observability Led Operations
- Use signals (metrics, logs, traces, events) to prevent incidents, not just react to them
- Apply observability data to guide AI reasoning, hypothesis testing, and remediation decisions
- Ensure observability is outcome driven, not tool driven
ITSM Aware AI Implementation
- Ensure AI solutions integrate with Incident, Problem, and Change processes
- Prevent AI from bypassing governance, controls, or operational approval flows
- Align AI automation with enterprise Run policies and service accountability
Production Operations Leadership
- Operate confidently in enterprise production environments with SLAs, major incidents, and change windows
- Bring strong understanding of operational risk, stability, and client accountability
- Reason and iterate effectively in messy, ambiguous environments with incomplete data
Outcome Driven Delivery
- Maintain a strict focus on measurable operational outcomes, not cool AI features
- Continuously evaluate and refine AI effectiveness based on Run performance signals
- Balance speed, automation, and resilience in live production contexts
Required Skills & Experience
AIOps & AI Capability
- Strong understanding of applying GenAI and Agentic AI to Run operations
- Ability to design agent orchestrated operational workflows
- Practical understanding of AI limitations and failure modes in production
Run & Operations Depth
- Hands on experience with enterprise Run operations, SLAs, incidents, and change management
- Strong understanding of ITSM processes and governance
- Observability led mindset across metrics, logs, traces, and events
Problem Solving & Ambiguity
- Comfortable working with incomplete, noisy, or imperfect operational data
- Strong reasoning and hypothesis driven troubleshooting skills
- Ability to iterate quickly and safely in live environments
Career Level Expectations CL7
- Acts as a senior AIOps authority across Run organizations
- Shapes how AI is applied safely and effectively in production operations
- Influences Run transformation without being tool or vendor centric
- Balances innovation with operational stability and trust
Success Measures
- Reduction in incident volume and alert noise
- Improved MTTR and operational resilience
- Increased automation and self healing coverage
- High trust in AI assisted Run operations from service leadership
Resource needs to be AI Ready.
Project Role Description : Own the integrity and governance of systems, including best practices for delivering services. Develop, deploy and support infrastructures, applications and technology initiatives from an architectural and operational perspective in conjunction with existing standards and methods of delivery.
Must have skills : AI & Data Solution Architecture
Good to have skills : IT Infrastructure Operations, Value Engineering (VE)
Minimum 12 year(s) of experience is required
Educational Qualification : 15 years full time education
Role Summary
AI Powered Tech Talent
Leads the design and adoption of AI first Run Operations, applying GenAI and Agentic AI to real production problems such as incident reduction, noise elimination, and self healing.
This role is deeply grounded in enterprise production operations, combining observability led thinking, agentic orchestration, and ITSM aware governance to deliver measurable operational outcomes, not PoCs or dashboards.
Acts as a senior technical and operational authority, able to reason in ambiguity and operate effectively in complex, imperfect Run environments.
Key Responsibilities
AI First Run Operations
- Apply GenAI and Agentic AI to day to day Run problems, including incident reduction, alert noise elimination, and self healing
- Focus on production outcomes, not experimentation (MTTR reduction, ticket deflection, automation coverage)
- Design AI solutions that augment Run teams while preserving operational trust and accountability
Agentic Orchestration & Automation
- Decompose complex Run workflows (incident diagnosis remediation validation) into agent driven, orchestrated steps
- Design agent interactions across diagnostic, remediation, and validation stages
- Replace static runbooks with dynamic, agent orchestrated workflows
Observability Led Operations
- Use signals (metrics, logs, traces, events) to prevent incidents, not just react to them
- Apply observability data to guide AI reasoning, hypothesis testing, and remediation decisions
- Ensure observability is outcome driven, not tool driven
ITSM Aware AI Implementation
- Ensure AI solutions integrate with Incident, Problem, and Change processes
- Prevent AI from bypassing governance, controls, or operational approval flows
- Align AI automation with enterprise Run policies and service accountability
Production Operations Leadership
- Operate confidently in enterprise production environments with SLAs, major incidents, and change windows
- Bring strong understanding of operational risk, stability, and client accountability
- Reason and iterate effectively in messy, ambiguous environments with incomplete data
Outcome Driven Delivery
- Maintain a strict focus on measurable operational outcomes, not cool AI features
- Continuously evaluate and refine AI effectiveness based on Run performance signals
- Balance speed, automation, and resilience in live production contexts
Required Skills & Experience
AIOps & AI Capability
- Strong understanding of applying GenAI and Agentic AI to Run operations
- Ability to design agent orchestrated operational workflows
- Practical understanding of AI limitations and failure modes in production
Run & Operations Depth
- Hands on experience with enterprise Run operations, SLAs, incidents, and change management
- Strong understanding of ITSM processes and governance
- Observability led mindset across metrics, logs, traces, and events
Problem Solving & Ambiguity
- Comfortable working with incomplete, noisy, or imperfect operational data
- Strong reasoning and hypothesis driven troubleshooting skills
- Ability to iterate quickly and safely in live environments
Career Level Expectations CL7
- Acts as a senior AIOps authority across Run organizations
- Shapes how AI is applied safely and effectively in production operations
- Influences Run transformation without being tool or vendor centric
- Balances innovation with operational stability and trust
Success Measures
- Reduction in incident volume and alert noise
- Improved MTTR and operational resilience
- Increased automation and self healing coverage
- High trust in AI assisted Run operations from service leadership
Resource needs to be AI Ready.
15 years full time education
Bengaluru
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