AIOps Leader

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:

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.

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