{"id":3955448,"url":"https://alion.io/job/airbus-aiops-leader-2","title":"AIOps Leader","company":{"id":3100,"name":"Airbus","domain":"airbus.com","url":"https://alion.io/company/airbus","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":83,"open_postings":837,"ghost_share":0.005,"stale_share":0.665,"repost_share":0.014,"time_to_fill_p50_days":26,"computed_at":"2026-11-09T06:03:00Z"}},"role":"DevOps","role_family":"DevOps","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AIOps","optional":false},{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Lambda","optional":false},{"name":"Azure","optional":false},{"name":"CloudFormation","optional":false},{"name":"Datadog","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Google Cloud Run","optional":false},{"name":"Google GKE","optional":false},{"name":"ITSM","optional":false},{"name":"Jira","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"OpenSearch","optional":false},{"name":"Pinecone","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"Qdrant","optional":false},{"name":"RAG","optional":false},{"name":"Self-Healing","optional":false},{"name":"ServiceNow","optional":false},{"name":"Terraform","optional":false},{"name":"Vertex AI","optional":false},{"name":"Vertex AI Agent Builder","optional":false},{"name":"Agile","optional":true},{"name":"ITIL","optional":true}],"status":"live","first_seen_at":"2026-11-09T07:35:20Z","employer_posted_date":null,"last_verified_at":"2026-11-09T07:35:20Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"Job Description:\n\nWe 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.\n\nIn 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.\n\nQualification & Experience:\n\nEducation: Bachelor's or Master's degree in Computer Science, Software Engineering, Information Technology, or a related quantitative field.\n\nOverall 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.\n\nPlatform Specialization: At least 3+ years architecting and running operations directly within major cloud ecosystems (AWS or GCP), including native AI/ML compute platforms.\n\nKey Responsibilities:\nArchitecture & Advanced AI/ML Automation\nSelf-Healing Infrastructure & Workflows: Architect, build, and deploy auto-remediation routines, script-based diagnostic runners, and event-driven automation triggers that autonomously resolve platform issues.\nLLM & 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.\nBot & 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.\nObservability, Telemetry & Predictive Analytics\nObservability 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.\nPredictive Anomaly Detection: Configure and tune real-time predictive alerting, log-pattern analysis, and AI-driven monitoring models across AWS, Azure, or GCP microservices.\nOperational 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.\nOperational Engineering & L1/L2 Empowerment\nToil Elimination: Continuously audit support operational bottlenecks across product teams, transforming high-volume manual intervention points into production-grade, single-click, or fully autonomous workflows.\nLog & 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.\nTechnical Mentorship: Guide L1/L2 support engineers on best practices for automation, code-based triage, and log parsing.\n\nTechnical Essentials:\nAWS & 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).\nCloud 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.\nContainerization & Orchestration: Production experience managing microservices via Kubernetes (EKS/GKE) and Docker to support agentic AI workers, vector indexing engines, and automated micro-tasks.\nObservability & 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.\nAdvanced 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).\nEnterprise 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.\n\nSoft Skills & Behavioral Attributes:\nTechnical Leadership & Influence: Proven ability to architect complex automation systems independently, establish technical standards, and convince cross-functional teams to adopt new operational models.\nRoot-Cause Obsession: Relentless drive to solve underlying architectural flaws rather than applying temporary operational workarounds.\nCross-Functional Bridge: Ability to speak fluidly with product developers, AI researchers, enterprise clients, and technical support teams.\nThrives in Ambiguity: Highly adaptable problem solver capable of establishing order, structure, and code standards in unstructured environments.\n\nNice-to-Have / Added Advantages:\nLinguistic Skill: Professional working proficiency or full fluency in French (spoken and written).\nCertifications: ITIL v5 Foundation or Managing Professional, Certified Agile Service Manager (CASM), PMP, or introductory Cloud/AI certifications (AWS Certified Cloud Practitioner, Azure AI Fundamentals).\nAI Bot Implementation Experience: Direct hands-on experience implementing conversational AI, RAG-based internal search, or auto-remediation bots within support portals.\n\nSuccess Metrics:\nMTTR (Mean Time to Resolution) - Reduction in overall incident MTTR via automated routing, diagnostic bots, and clear escalation paths.\nTicket Deflection & Reduction - Percentage of Tier-1 issues resolved via self-service bots, automated workflows, and root-cause resolution.\nSLA & First Contact Resolution - High compliance across overall uptime, response, and resolution SLAs for all AI products.\nOperational Centralization - Onboarding of existing and newly building AI products into the unified service delivery framework.\nStakeholder & CSAT Score - Satisfaction scores from internal product units, business leadership, and end-users.\n\nThis 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.\nSkills\nAIOps, AWS, AWS CloudFormation, Docker, GCP, Google Cloud, Kubernetes, Python, Terraform","description_format":"text","description_chars":7029,"description_truncated":false,"requirements":{"experience_years_min":7,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Aerospace Manufacturing","Satellite Manufacturing","Defense Manufacturing","Aircraft Manufacturing"],"lifecycle":[{"event":"open","at":"2026-11-09T07:35:20Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring 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