Technical Specialist - Cloud and Infrastructure Engineering

Birlasoft Limited; Bengaluru; On-site; INR 1,000,000 - 1,500,000; Full time

Job summary

Birlasoft Limited in Bengaluru is seeking a Mid-Level Agentic AI - Infrastructure Intelligence Analyst to transform operations from reactive support to predictive, proactive management. You will work with data, ITSM, and infrastructure teams to identify trends, automate patterns, and develop dashboards using Python, SQL, and Power BI, driving measurable reductions in MTTR and repetitive work.

This role emphasizes analytics, pattern recognition, and AI-driven improvements across Windows/Linux,

Qualifications

• Experience analyzing IT infrastructure data with Python/SQL.
• Familiarity with ITSM concepts and dashboards.
• Ability to identify patterns and automation opportunities.

Responsibilities

• Analyse historical ITSM data to identify recurring incidents.
• Correlate ticket data with monitoring and infra info.
• Develop dashboards and reports highlighting MTTR trends.
• Collaborate with infra SMEs to translate patterns into actions.
• Support AI use cases for predictive incident detection.
• Evaluate AI recommendations against infra standards.

Skills

Python

SQL

Data analysis

Machine Learning fundamentals

Statistical and trend analysis

Pattern recognition

Predictive analytics

Analytical and problem-solving skills

Tools

Power BI

Job description

Technical Specialist-Cloud & Infra Engg 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.

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

Experience Level Mid Level