Technical Specialist-Cloud & Infra Engg

Birlasoft; Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Birlasoft is seeking an Agentic AI – Infrastructure Intelligence Analyst to transform traditional infrastructure operations from reactive to proactive. You will analyze historical incident and ITSM data to uncover patterns and automation opportunities.

You will collaborate with infrastructure and AI teams to develop use cases, build dashboards, and validate recommendations, aiming to reduce MTTR and repeat incidents while enhancing proactive remediation.

Qualifications

• Experience in analysing ITSM data and incident trends.
• Proficiency in Python and SQL for data extraction and analysis.
• Familiarity with AI concepts like Agentic AI and predictive analytics.
• Ability to build dashboards and reports.

Responsibilities

• Analyse historical ITSM ticket dumps across incidents, requests, problems and changes.
• Identify recurring incidents, high-volume ticket categories and automation opportunities.
• Create dashboards highlighting MTTR trends and top incident drivers.
• Collaborate with SMEs to translate patterns into actionable problem statements.
• Validate AI recommendations against historical data and track outcomes.
• Support development of Agentic AI use cases for proactive remediation.

Skills

Python

SQL

Data analysis

ML fundamentals

Statistical analysis

Pattern recognition

Analytical thinking

Tools

Power BI

Job description

Area(s) of responsibility

We are looking for an Agentic AI – Infrastructure Intelligence Analyst to help transform traditional infrastructure operations from reactive support into predictive and proactive operations.

The role will focus on analysing historical incident, request, problem, alert and infrastructure ticket data, identifying recurring patterns and operational trends, and working with infrastructure and AI teams to develop proactive remediation and Agentic AI use cases.

Key Responsibilities

• Analyse historical ITSM ticket dumps across incidents, service requests, problems and changes.
• Identify:
• Recurring incidents
• High-volume ticket categories
• Repetitive manual activities
• Common root-cause patterns
• Infrastructure failure trends
• Seasonal and time-based patterns
• Potential automation opportunities
• Correlate ticket data with monitoring and infrastructure information.
• Use Python, SQL and analytical techniques to identify operational trends and anomalies.
• Create dashboards and reports highlighting:
• Top incident drivers
• Repeat issues
• MTTR trends
• Problem areas
• Automation candidates
• Proactive remediation opportunities
• Work with infrastructure SMEs to convert identified patterns into actionable problem statements.
• Support development of Agentic AI use cases for:
• Incident triage
• Root-cause analysis
• Knowledge recommendation
• Automated diagnostics
• Predictive incident detection
• Proactive remediation
• Help build and maintain operational knowledge bases, SOPs, runbooks and remediation patterns for AI agents.
• Validate AI recommendations against historical incidents and infrastructure standards.
• Track outcomes and measure reduction in repeat incidents and manual effort.
• Continuously identify new opportunities for AI-driven infrastructure operations.

Core Skills

• Python
• SQL
• Data analysis
• Machine Learning fundamentals
• Statistical and trend analysis
• Pattern recognition
• Predictive analytics
• Strong analytical and problem-solving skills

Infrastructure Knowledge – Good to Have

• Windows / Linux infrastructure
• Cloud platforms such as Azure or AWS
• Monitoring and observability concepts
• ITSM / Incident / Problem / Change Management
• Infrastructure automation
• ServiceNow or equivalent ITSM platforms
• Power BI or similar reporting tools

AI-based AI

• Agentic AI concepts
• Azure AI Foundry / Azure OpenAI / Amazon Bedrock
• Python-based AI frameworks

Typical Use Cases

Ticket Intelligence

Analyse 6–12 months of incident data and identify the top recurring infrastructure problems.

Pattern & Trend Detection

Identify recurring CPU, memory, disk, network, backup or application-support incidents.

Problem Prediction

Detect infrastructure patterns that typically precede a major incident.

Proactive Operations

Recommend health checks or remediation before an incident is raised.

Automation Discovery

Identify repetitive tickets suitable for scripts, Ansible, workflows or autonomous agents.

Agentic Incident Management

Enable AI agents to analyse alerts, search historical tickets and knowledge, recommend root cause and propose remediation.

Success Measures

• Reduction in repeat incidents
• Reduction in manual ticket analysis
• Increased automation opportunities identified
• Improved problem-management effectiveness
• Reduced MTTR
• Increased percentage of incidents proactively prevented
• Increased reusable remediation patterns and knowledge articles