{"id":1560054,"url":"https://alion.io/job/allegion-senior-ai-operations-engineer","title":"Senior AI Operations Engineer","company":{"id":9343,"name":"Allegion","domain":"allegion.com","url":"https://alion.io/company/allegion","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":82,"open_postings":56,"ghost_share":0,"stale_share":0.696,"repost_share":0.036,"time_to_fill_p50_days":28,"computed_at":"2026-10-20T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","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":{"min_usd":21000,"max_usd":43000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":38},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"Azure AKS","optional":false},{"name":"Azure DevOps","optional":false},{"name":"Bash","optional":false},{"name":"CI/CD","optional":false},{"name":"DNS","optional":false},{"name":"Docker","optional":false},{"name":"Git","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Incident Management","optional":false},{"name":"Kubernetes","optional":false},{"name":"Least Privilege","optional":false},{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"Platform Engineering","optional":false},{"name":"PowerShell","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Terraform","optional":false},{"name":"Vertex AI","optional":false},{"name":"ArgoCD","optional":true},{"name":"CDKTF","optional":true},{"name":"GitOps","optional":true},{"name":"Grafana","optional":true},{"name":"Helm","optional":true},{"name":"Kustomize","optional":true},{"name":"LiteLLM","optional":true},{"name":"Microsoft Entra ID","optional":true},{"name":"Prometheus","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-10-01T03:36:45Z","employer_posted_date":"2026-10-01","last_verified_at":"2026-10-20T13:26:40Z","board_verified":true,"closed_at":null,"days_open":19,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":19},"description":"Creating Peace of Mind by Pioneering Safety and Security\nAt Allegion, we help keep the people you know and love safe and secure where they live, work and visit. With more than 40 brands, 14,000+ employees globally and products sold in 130 countries, we specialize in security around the doorway and beyond.\nAdditionally, Allegion is proud to be recognized with the 2026 Gallup Exceptional Workplace Award (GEWA) for the third consecutive year, earning distinction in both the employee engagement and strengths categories. This year, Allegion also received Gallup’s With Distinction honor - a designation reserved for a select group of organizations that go above and beyond in building exceptional workplace cultures.\nKEY RESPONSIBILITIES\nAI Platform Operations\nAI Platform Administration\nOperate and maintain enterprise AI platforms and services across development, test, and production environments. \nSupport Azure OpenAI, Azure AI Foundry, Azure Machine Learning, and related Azure AI platform services. \nOperate the application hosting platform for AI services across Azure App Service, Azure Functions, and Azure Container Apps, including networking, scaling, and runtime configuration. \nEnsure platform availability, performance, reliability, scalability, and operational stability. \nPerform platform configuration, lifecycle management, upgrades, and operational maintenance. \nAI Service Deployment & Operations\nDeploy and operationalize AI and Generative AI workloads in controlled enterprise environments. \nSupport production AI solutions including copilots, AI assistants, chatbots, agentic applications, retrieval-augmented generation (RAG) services, and machine learning applications. \nManage release activities, prompt and model version control, runtime configurations, deployment validations, and operational handovers. \nDeploy and operate machine learning models and endpoints developed by Data Science teams, providing the pipelines, environments, and runtime platform they promote through. \nTroubleshoot deployment, integration, performance, and runtime issues across AI services. \nContainer & Kubernetes Platform\nSupport containerised AI workloads using Docker and Kubernetes operational best practices, including image build, hardening, registries, and runtime configuration. \nHelp design, build, and operate Azure Kubernetes Service environments as the container platform is introduced for AI and platform workloads. \nEstablish and monitor cluster health, capacity, autoscaling, networking, ingress, security policies, and operational readiness. \nContribute to platform resiliency, backup, recovery, and disaster recovery readiness. \nModel Access & Gateway Operations\nOperate the governed model-access layer serving AI applications, agents, and developer tooling - provider routing, failover, model catalogue, and version management across Azure OpenAI, AWS Bedrock, and Google Vertex AI. \nManage model access control, user and application entitlements, quota allocation, rate limiting, and credential provisioning for the gateway. \nMonitor and attribute token consumption, throttling and 429 events, latency, and cost by model, application, and consumer. \nSupport gateway upgrades, provider onboarding, configuration changes, and controlled rollout of new model versions. \nMonitoring, Reliability & Support\nMonitoring & Observability\nImplement monitoring, logging, alerting, dashboards, and operational metrics for AI platforms and services. \nMonitor AI system utilization, availability, latency, cost consumption, token usage, and performance trends. \nMonitor AI-specific signals including model and agent quality, quota and throttling events, data or model drift, and grounding index freshness. \nUse Azure Monitor, Application Insights, Log Analytics, and OpenTelemetry instrumentation to identify incidents and improvement areas. \nProvide operational insights to engineering, security, governance, and leadership stakeholders. \nIncident & Problem Management\nInvestigate and resolve platform incidents, production issues, service disruptions, and operational risks. \nPerform root cause analysis and document corrective and preventive actions. \nSupport production support processes, escalation management, and incident communications. \nParticipate in post-incident reviews and implement service reliability improvements. \nOperational Excellence\nCreate and maintain runbooks, support procedures, platform documentation, and operational knowledge articles. \nAutomate recurring support, validation, monitoring, and deployment activities. \nContinuously improve platform efficiency, stability, security posture, and cost governance. \nSupport service-level objectives and operational performance targets for AI platforms. \nCloud Engineering & Automation\nInfrastructure as Code\nDevelop and maintain Infrastructure as Code using Terraform, including reusable modules, remote state management, validation, and peer review. \nAutomate Azure AI platform provisioning, configuration, policy enforcement, and environment standardization across development, test, and production. \nManage controlled promotion of infrastructure changes across environments, with drift detection, change tracking, and infrastructure compliance. \nSupport configuration consistency, version control, and secure state management for all platform infrastructure. \nCI/CD & Release Engineering\nBuild and maintain CI/CD pipelines for AI platform services, application workloads, containers, and supporting infrastructure. \nSupport deployment automation using Azure DevOps, GitHub Actions, or equivalent tools, including approvals, artifact management, and tested rollback. \nImplement validation checks, release controls, and operational readiness gates, including automated model and prompt evaluations, content safety, latency, and cost checks before production release. \nBring unmanaged or manually deployed services under governed, automated delivery. \nMaintain traceability and reproducibility across code, configuration, models, prompts, evaluation results, and deployed versions. \nDeveloper Enablement & Self-Service\nBuild and maintain reusable Terraform modules, pipeline templates, and standardized provisioning patterns consumed by other engineering teams. \nEstablish golden paths and self-service capabilities that allow AI and application teams to onboard workloads with reduced manual effort and lower operational risk. \nPromote reusable platform patterns and controlled deployment models across environments. \nMaintain platform documentation, module usage guidance, and onboarding references for consuming teams. \nSecurity & Identity Management\nImplement secure cloud configurations and platform controls for AI services, including private networking, private endpoints, and network security controls. \nSupport identity and access management using managed identities, workload identities, RBAC, and least-privilege practices. \nManage secrets, certificates, keys, rotation, and secure configuration using Azure Key Vault or approved tools. \nImplement and operate privileged access controls, including just-in-time elevation and periodic entitlement review. \nRemediate identified platform security and hardening findings, and prevent recurrence through platform defaults, policy, and pipeline checks. \nPartner with Information Security teams to address vulnerabilities, access reviews, and security findings.\nREQUIRED QUALIFICATIONS\nEducation\nBachelor's degree in Computer Science, Information Technology, Engineering, Data Science, or a related STEM discipline, or equivalent practical experience. \nExperience\n8+ years of experience in Cloud Platform Engineering, Infrastructure Engineering, DevOps, Platform Operations, SRE, MLOps, or AI Operations. \nHands-on experience supporting Microsoft Azure-based enterprise environments and production workloads. \nExperience operating AI, Machine Learning, or Generative AI platforms in controlled production environments. \nDemonstrated experience owning a shared platform, infrastructure module library, or delivery pipelines consumed by other engineering teams - not solely application-level delivery. \nExperience supporting Kubernetes-based solutions and containerized workloads, preferably Azure Kubernetes Service. \nExposure to AI governance, information security, compliance, operational risk, and production support practices. \nREQUIRED SKILLS\nTechnical Skills\nStrong hands-on experience with Microsoft Azure cloud services and production cloud operations. \nHands-on experience operating Azure application hosting services - App Service, Azure Functions, and container-based compute. \nExperience with Azure OpenAI, Azure AI Foundry, Azure Machine Learning, and related Azure AI platform services. \nExperience with containerized workloads using Docker, and working knowledge of Kubernetes sufficient to operate and build toward a production cluster platform. \nStrong understanding of cloud networking, private endpoints, DNS, NSGs, load balancing, and secure platform architecture. \nExperience with Infrastructure as Code, preferably Terraform, including reusable modules, remote state, and automated environment provisioning. \nExperience with Azure DevOps or GitHub Actions for CI/CD pipeline design, release management, and deployment automation. \nAutomation and scripting proficiency in Python, and at least one of Bash or PowerShell. \nStrong Linux, identity and access management, secret management, API, and Git skills. \nKnowledge of monitoring, observability, logging, alerting, incident management, and root cause analysis. \nUnderstanding of the AI/ML operational lifecycle, model and service deployment, runtime support, and platform management. \nWorking knowledge of generative AI components including prompt and model versioning, agents, RAG, vector stores, evaluations, groundedness, and content safety controls. \nFamiliarity with AI governance, responsible AI, privacy, security, and compliance best practices. \nPreferred Skills & Experience\nHands-on Azure Kubernetes Service operations: cluster build and upgrade, node pools, ingress, autoscaling, network and security policy, and production troubleshooting. \nExperience operating multi-provider model access across AWS Bedrock and Google Vertex AI alongside Azure OpenAI, and gateway technologies such as LiteLLM. \nAzure API Management policy authoring, including token validation, rate limiting, and backend protection. \nHelm or Kustomize; GitOps tooling such as Argo CD or Flux; Prometheus or Azure Managed Grafana. \nTerraform CDK (CDKTF) in TypeScript, or comparable programmatic infrastructure as code. \nMicrosoft Entra ID governance, including Privileged Identity Management and Conditional Access. \nAzure AI Search, vector databases, and event-driven or API-driven application architectures. \nExperience building internal developer platforms or self-service engineering capabilities. \nTools & Platforms Preferred\nAzure OpenAI \nAzure AI Foundry \nAzure Machine Learning \nAzure Kubernetes Service \nAzure App Service \nAzure Functions \nAzure Container Apps \nAzure API Management \nTerraform \nAzure DevOps \nDocker \nAzure Container Registry \nAzure Monitor \nApplication Insights \nLog Analytics \nAzure Key Vault \nAnalytical & Problem-Solving Skills\nStrong troubleshooting, analytical thinking, and root cause analysis capabilities. \nAbility to resolve complex production issues across cloud, platform, AI service, and integration layers. \nAbility to analyze operational metrics, identify trends, and recommend reliability and cost optimization improvements. \nStrong ownership mindset with the ability to work independently in an individual contributor capacity. \nCommunication & Soft Skills\nStrong written and verbal communication skills with technical and non-technical stakeholders. \nAbility to collaborate across Data & AI, Enterprise Architecture, Information Security, Governance, Cloud, and business teams. \nStrong documentation, follow-up, prioritization, and stakeholder engagement 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