AIOps Leader

AIOps Leader

Job Description:

Job Description - JR

Job Description Date: August 2026 

Role : AIOps Leader

Number of positions : 1 

Description: 

We are seeking a seasoned, hands-on AIOps Leader (7+ Years of Experience) to serve as the principal technical architect and lead builder for our centralized AI Product Support & Operations function. Operating within a high-growth AI PSL (Product/Service Line), you will design, architect, and execute the end-to-end automation strategy that transforms raw operational chaos into scalable, self-healing, and data-driven infrastructure.

In this role, you will bridge software engineering, site reliability, and AI/ML architectures. You will lead the creation of intelligent diagnostic pipelines, custom RAG-driven knowledge tools, self-healing systems, and automated triage engines. You will work closely with cross-functional leadership, L1/L2 support teams, and platform engineering to systematically eliminate operational toil, optimize MTTR, and build proactive anomaly detection mechanisms across our AI ecosystem.

Qualification & Experience: 

Education: Bachelor’s or Master’s degree in Computer Science, Software Engineering, Information Technology, or a related quantitative field.

Overall Experience: 7+ years of hands-on experience across Software Engineering, Site Reliability Engineering (SRE), DevOps, or Systems Operations—with a focused concentration on cloud infrastructure automation and AI/ML operational tooling.

Platform Specialization: At least 3+ years architecting and running operations directly within major cloud ecosystems (AWS or GCP), including native AI/ML compute platforms.

Key Responsibilities 

1. Architecture & Advanced AI/ML Automation

• Self-Healing Infrastructure & Workflows: Architect, build, and deploy auto-remediation routines, script-based diagnostic runners, and event-driven automation triggers that autonomously resolve platform issues.

• LLM & RAG Systems Engineering: Design, implement, and maintain advanced Retrieval-Augmented Generation (RAG) knowledge tools, vector databases, and LLM utilities that index telemetry, historic logs, and RCAs for instant incident context.

• Bot & Agent Development: Lead the development and production rollout of conversational AI agents, custom webhooks, and self-service bots integrated into ticketing engines to automate Tier-1 and Tier-2 resolutions.

2. Observability, Telemetry & Predictive Analytics

• Observability Architecture: Build enterprise-grade telemetry ingestion workflows, automated log scraping, and context-enrichment pipelines that dynamically append system metrics directly to incident tickets upon creation.

• Predictive Anomaly Detection: Configure and tune real-time predictive alerting, log-pattern analysis, and AI-driven monitoring models across AWS, Azure, or GCP microservices.

• Operational BI & Analytics: Architect and own centralized executive and operational dashboards (e.g., ServiceNow, Datadog) tracking MTTR velocity, system uptime, defect density, ticket deflection rates, and SLA/CSAT compliance.

3. Operational Engineering & L1/L2 Empowerment

• Toil Elimination: Continuously audit support operational bottlenecks across product teams, transforming high-volume manual intervention points into production-grade, single-click, or fully autonomous workflows.

• Log & Metadata Standardization: Standardize system log outputs, stack-trace formatting, and tagging taxonomy across all AI products to ensure platform telemetry remains machine-readable for AI engines.

• Technical Mentorship: Guide L1/L2 support engineers on best practices for automation, code-based triage, and log parsing.

Technical Essentials

• AWS & GCP Native AI Platforms: Deep hands-on experience orchestrating production AI/ML workflows on AWS (Bedrock, SageMaker AI, OpenSearch, AWS Lambda) or GCP (Vertex AI, Vertex AI Agent Builder, Cloud Run, BigQuery).

• Cloud Infrastructure & Infrastructure-as-Code (IaC): Advanced experience writing and managing cloud provisioning scripts using Terraform, AWS CloudFormation, or Google Cloud Deployment Manager to deploy auto-scaling, resilient operations environments.

• Containerization & Orchestration: Production experience managing microservices via Kubernetes (EKS/GKE) and Docker to support agentic AI workers, vector indexing engines, and automated micro-tasks.

• Observability & Cloud Telemetry: Proven capability to configure full-stack observability across cloud environments using AWS CloudWatch or GCP Cloud Logging/Monitoring to trigger automated alerts and log enrichment.

• Advanced Automation & Scripting: Strong engineering capability in Python, Go, or Shell to build autonomous cloud functions (AWS Lambda/GCP Cloud Run), self-healing infrastructure scripts, and custom ITSM connectors (Jira/ServiceNow APIs).

• Enterprise Generative AI Stack: Production execution experience deploying RAG (Retrieval-Augmented Generation) architectures using cloud vector engines (Amazon Bedrock Knowledge Bases, GCP Vertex AI Search, Pinecone, or Qdrant) for automated incident context retrieval.

Soft Skills & Behavioral Attributes

• Technical Leadership & Influence: Proven ability to architect complex automation systems independently, establish technical standards, and convince cross-functional teams to adopt new operational models.

• Root-Cause Obsession: Relentless drive to solve underlying architectural flaws rather than applying temporary operational workarounds.

• Cross-Functional Bridge: Ability to speak fluidly with product developers, AI researchers, enterprise clients, and technical support teams.

• Thrives in Ambiguity: Highly adaptable problem solver capable of establishing order, structure, and code standards in unstructured environments.

Nice-to-Have / Added Advantages

• Linguistic Skill: Professional working proficiency or full fluency in French (spoken and written).

• Certifications: ITIL v5 Foundation or Managing Professional, Certified Agile Service Manager (CASM), PMP, or introductory Cloud/AI certifications (AWS Certified Cloud Practitioner, Azure AI Fundamentals).

• AI Bot Implementation Experience: Direct hands-on experience implementing conversational AI, RAG-based internal search, or auto-remediation bots within support portals.

Success Metrics

• MTTR (Mean Time to Resolution) - Reduction in overall incident MTTR via automated routing, diagnostic bots, and clear escalation paths .

• Ticket Deflection & Reduction - Percentage of Tier-1 issues resolved via self-service bots, automated workflows, and root-cause resolution.

• SLA & First Contact Resolution - High compliance across overall uptime, response, and resolution SLAs for all AI products. 

• Operational Centralization - Onboarding of existing and newly building AI products into the unified service delivery framework.

• Stakeholder & CSAT Score - Satisfaction scores from internal product units, business leadership, and end-users.

This job requires an awareness of any potential compliance risks and a commitment to act with integrity, as the foundation for the Company’s success, reputation and sustainable growth.

Company:

Airbus India Private Limited

Employment Type:

Permanent

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Experience Level:

Professional

Job Family:

Digital

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