{"id":3159616,"url":"https://alion.io/job/confido-health-devops-engineer","title":"DevOps Engineer","company":{"id":1280,"name":"Confido Health","domain":"confido.health","url":"https://alion.io/company/confido-health","size_band":"11-50","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":"middle","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":{"min_usd":12000,"max_usd":34000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":30},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"ArgoCD","optional":false},{"name":"Bash","optional":false},{"name":"Blue-Green Deployment","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"FinOps","optional":false},{"name":"GCP","optional":false},{"name":"GitLab CI","optional":false},{"name":"Google Cloud Run","optional":false},{"name":"Google GKE","optional":false},{"name":"Grafana","optional":false},{"name":"HIPAA","optional":false},{"name":"IAM","optional":false},{"name":"Incident Management","optional":false},{"name":"Istio","optional":false},{"name":"Kubernetes","optional":false},{"name":"Least Privilege","optional":false},{"name":"Linux","optional":false},{"name":"LLM","optional":false},{"name":"LLMOps","optional":false},{"name":"Loki","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"Service Mesh","optional":false},{"name":"SigNoz","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"SOC 2","optional":false},{"name":"Terraform","optional":false},{"name":"Twelve-Factor App","optional":false}],"status":"live","first_seen_at":"2026-10-23T15:35:27Z","employer_posted_date":null,"last_verified_at":"2026-10-23T15:35:27Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"The core responsibilities for the job include the following:\n\nCloud Infrastructure Ownership: \nOwn the design, evolution and day-to-day operation of cloud infrastructure on GCP.\nManage multi-environment infrastructure (dev/staging/prod/preview/playground) with clear isolation and promotion paths.\nBuild and maintain Infrastructure-as-Code, with reusable modules and best practices.\nEnsure high availability, scalability, backups, disaster recovery and secure access controls.\nAct as a point of escalation for infrastructure and platform-related issues.\n\nBackend and Data Ops: \nOperate and support production microservices running on Kubernetes (GKE).\nSupport the reliability and scalability of data systems and pipelines from an infra/ops standpoint.\nOwn operational aspects of databases and background jobs, queues and workflows.\nPartner with backend and data engineers to improve performance, failure isolation, and observability with ease of debugging in production.\n\nRelease Engineering: \nDesign, maintain and evolve CI/CD pipelines.\nEnable one-click deployments, safe release strategies (blue-green, canary, rollback) and environment promotion workflows.\nBuild and maintain ephemeral preview environments per PR.\nImprove developer experience through standardised service templates, opinionated base images and self-service infrastructure tooling.\nContinuously reduce deployment friction and operational toil.\n\nObservability: \nOwn end-to-end observability across services using OpenTelemetry.\nDesign and maintain dashboards, alerts and on-call hygiene.\nDefine and track SLIs, SLOs, SLAs and error budgets.\nLead incident response, postmortems and reliability improvements.\nEnsure all systems follow 12-factor app principles and are debuggable by default.\n\nSecurity and Compliance: \nApply security-by-default and least-privilege principles across cloud, Kubernetes and CI/CD.\nOwn and manage IAM, secrets management and access controls for production systems.\nEnsure encryption in transit and at rest for services, databases and data pipelines.\nSecure Kubernetes workloads using RBAC, namespace isolation, network policies, and container best practices.\nIntegrate vulnerability scanning and security checks into CI/CD pipelines.\nEnsure PHI/PII is handled safely across applications, data platforms and AI systems.\nMaintain audit trails and traceability for production changes and access.\nSupport infrastructure practices aligned with HIPAA and SOC2 security requirements.\n\nMLOps and LLMOps: \nEnable production deployment of fine-tuned LLM models.\nSupport GPU-backed inference workloads via GPU node pools, autoscaling and cost-aware scheduling.\nHelp standardise patterns for model serving, model versioning, rollout strategies and canary or shadow deployments for models.\nEnsure AI services have proper observability for latency, throughput, error rates and cost per request.\nDrive FinOps practices, including compute and GPU cost optimisation, right-sizing, storage and network efficiency.\n\nRequirements: \n3+ years of hands-on experience in DevOps/Platform/SRE roles.\nStrong, production-level experience with GCP (Compute, IAM, VPC, Cloud Run, etc. ).\nDeep experience with Docker and Kubernetes (GKE).\nProven ownership of CI/CD pipelines (GitLab CI + ArgoCD) and release workflows.\nExperience with Infrastructure-as-Code (Terraform) and service mesh (Istio).\nExperience with OpenTelemetry and tools like Signoz, Prometheus, Grafana, ELK, Loki, etc.\nStrong Linux fundamentals and scripting skills (Bash, Python).\nExperience operating production systems with uptime and latency requirements.\nStrong debugging, incident management and root cause analysis skills.\nAbility to work independently and make sound technical decisions.\n\nBonus: \nExposure to ML infrastructure, MLOps or LLMOps.\nExperience running GPU workloads or inference services.\nFamiliarity with data platforms or workflow engines.\nExperience in regulated domains (healthcare, fintech, etc. ).\nSkills\nGCP, GKE, Google Cloud, Kubernetes, 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