{"id":3900027,"url":"https://alion.io/job/iderize-devops-lead","title":"DevOps Lead","company":{"id":3800771,"name":"Iderize","domain":"techjays.com","url":"https://alion.io/company/iderize","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"DevOps","role_family":"DevOps","seniority":"lead","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Coimbatore, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Git","optional":false},{"name":"Kubernetes","optional":false},{"name":"Linux","optional":false},{"name":"Terraform","optional":false},{"name":"Amazon CloudWatch","optional":true},{"name":"Amazon EC2","optional":true},{"name":"Amazon EKS","optional":true},{"name":"Amazon S3","optional":true},{"name":"Anthropic","optional":true},{"name":"ArgoCD","optional":true},{"name":"AWS Lambda","optional":true},{"name":"Azure AKS","optional":true},{"name":"Azure DevOps","optional":true},{"name":"Bash","optional":true},{"name":"Bicep","optional":true},{"name":"Claude","optional":true},{"name":"CloudFormation","optional":true},{"name":"Configuration Management","optional":true},{"name":"FinOps","optional":true},{"name":"GitHub Actions","optional":true},{"name":"GitLab","optional":true},{"name":"GitLab CI","optional":true},{"name":"GitOps","optional":true},{"name":"Grafana","optional":true},{"name":"Helm","optional":true},{"name":"IAM","optional":true},{"name":"Incident Management","optional":true},{"name":"Jenkins","optional":true},{"name":"LLM","optional":true},{"name":"OpenAI","optional":true},{"name":"Platform Engineering","optional":true},{"name":"PowerShell","optional":true},{"name":"Prometheus","optional":true},{"name":"Python","optional":true},{"name":"RAG","optional":true},{"name":"Unix","optional":true},{"name":"Windows","optional":true}],"status":"live","first_seen_at":"2026-11-06T13:13:22Z","employer_posted_date":null,"last_verified_at":"2026-11-06T13:13:22Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"TECHJAYS\n\nTHE AI REIMAGINATION COMPANY\n\nPosition DevOps Lead\nExperience 10+ Years\nLocation Coimbatore / Hybrid\nJoining Immediate / ASAP\n\nPrimary SkillsDevOps, AWS or Azure, CI/CD, Kubernetes, Docker, Terraform, Linux, Git, Cloud Infrastructure, Infrastructure Automation, Monitoring & Observability\nPreferred SkillsAI/ML Infrastructure Exposure, GenAI Applications, GitOps, Argo CD, Prometheus, Grafana, Python, Bash, PowerShell, Cloud Security, FinOps\n\nAbout Techjays\nAt Techjays, we're helping businesses reimagine how they build, operate, and scale with AI at the core. We focus on developing secure, scalable, production-grade AI systems that solve complex business challenges and deliver measurable business outcomes.\nOur team brings together experienced engineers and technology leaders from organizations such as Google, NetApp, ADP, Cognizant Consulting, and Capgemini. We work with global clients to build advanced AI-powered applications, enterprise platforms, and intelligent automation solutions.\nWe're looking for a DevOps Lead who can take ownership of cloud infrastructure, deployment automation, CI/CD pipelines, platform reliability, and engineering operations.\nThe ideal candidate will have deep hands-on expertise in either AWS or Azure, along with strong experience in Kubernetes, containerization, Infrastructure as Code, and production cloud environments.\nYou will lead the design and implementation of reliable infrastructure platforms, establish DevOps best practices, and work closely with software engineering and AI teams to support the deployment and operation of modern applications.\nThis role requires a strong hands-on cloud and DevOps specialist who can architect, implement, troubleshoot, and optimize production infrastructure. Prior exposure to AI/ML or AI-enabled applications is required, but specialized MLOps expertise is not mandatory.\nKey Responsibilities\n1. Cloud Infrastructure & Architecture\nLead the design, implementation, and management of scalable, secure, and highly available cloud infrastructure.\nOwn cloud infrastructure architecture and operations across AWS or Azure.\nDesign and manage cloud environments covering compute, networking, storage, databases, and container platforms.\nEstablish infrastructure standards, architecture patterns, and operational best practices.\nManage cloud resource provisioning, configuration, scaling, and environment management.\nEnsure infrastructure meets requirements for availability, performance, security, and reliability.\nDrive infrastructure improvements through automation and continuous optimization.\n2. DevOps & CI/CD Engineering\nDesign, implement, and maintain CI/CD pipelines for application development and deployment.\nManage multi-stage deployment workflows across development, testing, staging, and production environments.\nImplement automated build, testing, deployment, and release processes.\nEstablish reusable pipeline templates, deployment standards, and approval workflows.\nManage deployment strategies, including rolling, blue/green, and canary deployments.\nImplement rollback mechanisms and ensure reliable production releases.\nIntegrate CI/CD workflows with source control, artifact repositories, and container registries.\nContinuously improve deployment efficiency, release reliability, and engineering productivity.\n3. Containerization & Kubernetes Management\nArchitect and manage containerized application environments using Docker and Kubernetes.\nManage production Kubernetes clusters using Amazon EKS or Azure AKS.\nImplement Kubernetes configurations covering deployments, services, networking, secrets, storage, and resource management.\nWork with Helm charts for application packaging and deployment.\nOptimize container resource utilization, application scaling, and cluster performance.\nTroubleshoot complex container and Kubernetes-related production issues.\nEstablish best practices for container security and workload reliability.\n4. Infrastructure as Code & Automation\nImplement Infrastructure as Code using Terraform and cloud-native provisioning tools.\nAutomate infrastructure provisioning, configuration management, and environment setup.\nDevelop reusable infrastructure modules and deployment automation workflows.\nUse Python, Bash, or PowerShell to automate operational tasks and improve efficiency.\nReduce manual intervention through standardization and automation.\nMaintain infrastructure documentation, configuration standards, and version-controlled deployment workflows.\n5. Monitoring, Reliability & Production Operations\nEstablish centralized monitoring, logging, alerting, and observability across cloud environments.\nImplement monitoring solutions using CloudWatch, Azure Monitor, Prometheus, Grafana, or equivalent platforms.\nMonitor application availability, infrastructure health, resource utilization, and system performance.\nLead troubleshooting and resolution of complex production incidents.\nConduct root cause analysis and implement permanent corrective actions.\nEstablish operational practices for backup, disaster recovery, availability, and business continuity.\nImprove system reliability through proactive monitoring and performance optimization.\n6. Cloud Security & Governance\nImplement cloud security best practices across infrastructure and application environments.\nManage IAM, RBAC, access controls, service identities, and secrets management.\nSecure cloud networking, virtual networks, firewalls, and infrastructure access.\nImplement container security and infrastructure hardening practices.\nEnsure cloud environments follow organizational security and compliance requirements.\nIdentify infrastructure vulnerabilities and coordinate remediation activities.\nEstablish secure deployment and configuration management practices.\n7. AI Infrastructure & Engineering Collaboration\nWork closely with AI/ML and software engineering teams to provide infrastructure for AI-powered applications.\nSupport the deployment and operation of LLM-based applications, AI services, and AI-enabled platforms.\nDesign cloud infrastructure capable of supporting AI workloads, including compute, storage, networking, and containerized services.\nSupport the infrastructure requirements of AI applications across development, testing, and production environments.\nCollaborate with engineering teams to improve deployment reliability, scalability, and operational performance of AI-powered systems.\nEvaluate infrastructure technologies and tools that support evolving AI application requirements.\n8. Technical Leadership & Team Development\nProvide technical leadership to DevOps engineers and guide implementation across projects.\nEstablish engineering standards, infrastructure best practices, and operational procedures.\nMentor team members on cloud architecture, automation, CI/CD, Kubernetes, and production operations.\nLead technical discussions and make architecture decisions involving infrastructure and DevOps technologies.\nCollaborate with engineering, security, and product teams to align infrastructure capabilities with business requirements.\nDrive continuous improvement across DevOps processes, tools, and engineering practices.\nRequired Technical Skills & Experience\n1. Cloud Platforms\n10+ years of overall experience in DevOps, cloud infrastructure, or platform engineering.\nStrong hands-on expertise in at least one major cloud platform: AWS or Azure.\nPractical experience designing, deploying, and operating production-grade cloud infrastructure.\nAWS: EC2, S3, IAM, VPC, EKS, Lambda, ECR, CloudWatch, and related infrastructure services.\nAzure: Azure Virtual Machines, VNets, AKS, Azure DevOps, Azure Container Registry, Azure Monitor, Key Vault, and related infrastructure services.\nCandidates do not need to be experts in both AWS and Azure. Deep expertise in either platform is the priority.\n2. DevOps & CI/CD\nStrong experience designing and maintaining CI/CD pipelines.\nHands-on experience with Azure DevOps, Jenkins, GitHub Actions, GitLab CI, or equivalent tools.\nUnderstanding of multi-stage pipelines, automated deployments, release approvals, and artifact management.\nExperience implementing deployment automation and rollback strategies.\nStrong understanding of Git workflows and version control practices.\n3. Containers & Kubernetes\nStrong hands-on experience with Docker and Kubernetes.\nPractical experience managing EKS or AKS environments.\nUnderstanding of Kubernetes networking, scaling, storage, secrets, and workload management.\nExperience with Helm and container orchestration.\nAbility to troubleshoot production container and Kubernetes environments.\n4. Infrastructure as Code & Scripting\nStrong experience with Terraform and Infrastructure as Code practices.\nFamiliarity with CloudFormation, Bicep, ARM templates, or similar tools.\nWorking knowledge of Python, Bash, or PowerShell for infrastructure automation.\nStrong understanding of Linux administration and system troubleshooting.\n5. Monitoring & Reliability Engineering\nExperience implementing infrastructure and application monitoring.\nFamiliarity with CloudWatch, Azure Monitor, Prometheus, Grafana, and centralized logging solutions.\nUnderstanding of incident management, root cause analysis, performance optimization, and system availability.\nExperience establishing alerting, operational dashboards, and reliability practices.\n6. AI/ML Infrastructure Exposure\nPrior experience supporting AI/ML applications, Generative AI platforms, LLM applications, or AI-enabled products.\nUnderstanding of infrastructure requirements for deploying and operating AI-powered applications.\nFamiliarity with containerized AI services, cloud-based model integrations, and scalable application environments.\nNote: This role does not require specialized MLOps expertise or model development experience. Strong cloud and DevOps engineering fundamentals remain the primary requirement.\nPreferred Skills\nExperience implementing GitOps workflows using Argo CD or similar tools.\nExperience with blue/green deployments, canary releases, and automated rollback mechanisms.\nExposure to cloud security, enterprise networking, governance, and landing-zone architecture.\nExperience with cloud cost optimization and FinOps practices.\nFamiliarity with AI/ML infrastructure and production GenAI applications.\nExperience with Python-based automation and infrastructure tooling.\nRelevant AWS, Azure, Kubernetes, or Terraform certifications.\nWhat We're Looking For Beyond Skills\nCloud Expertise: Deep technical specialization in AWS or Azure, with the ability to design and manage production infrastructure independently.\nHands-on Engineering: Someone who actively builds, automates, troubleshoots, and improves infrastructure rather than focusing only on team management.\nTechnical Ownership: Takes responsibility for infrastructure reliability, deployment quality, security, and operational performance.\nSystem Thinking: Understands how cloud infrastructure, applications, networking, databases, and deployment pipelines work together.\nProblem-Solving: Ability to diagnose complex production issues and implement long-term engineering solutions.\nAutomation Mindset: Continuously identifies opportunities to eliminate manual work and improve operational efficiency.\nTechnical Leadership: Can mentor engineers, establish best practices, and drive technical decisions.\nAI Readiness: Has practical exposure to supporting AI-powered applications and understands their infrastructure requirements.\nCollaboration: Works effectively with software engineering, AI/ML, security, and product teams.\nTypical Technology Environment\nAreaTechnologiesCloud PlatformsAWS, AzureCI/CDAzure DevOps, GitHub Actions, Jenkins, GitLab CIContainersDocker, Kubernetes, EKS, AKS, HelmInfrastructure as CodeTerraform, 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