Senior DevOps Engineer (AI + Azure)
EY
Senior DevOps Engineer (AI + Azure)
Job Description
About Us
At EY wavespace Madrid - AI & Data Hub, we are a diverse, multicultural team at the forefront of technological innovation, working with cutting-edge technologies like Gen AI, data analytics, robotics, etc. Our center is dedicated to exploring the future of AI and Data.
Overview:
We’re looking for a Senior DevOps Engineer to build and run cloud and AI infrastructure at scale. You’ll own IaC with Terraform, CI/CD, Kubernetes, and Linux. You’ll also help run LLM workloads both in Azure and locally (Ollama/vLLM/llama.cpp). Your work will enable fast, secure, repeatable delivery.
Key responsibilities
- Build and maintain Azure infrastructure with Terraform (modules, workspaces, pipelines, policies).
- Design and operate CI/CD with GitHub Actions and/or Azure DevOps (multi-stage, approvals, environments).
- Run containers and Kubernetes/AKS (Helm, ingress, autoscaling, node pools, storage).
- Manage AI/LLM runtime: local model runners (Ollama, vLLM, llama.cpp), GPU/CPU configs.
- Support RAG: embeddings pipelines, vector DBs (Azure AI Search/Cognitive Search, pgvector, Milvus), data sync, retention.
- Automate platform tasks with Python (tooling, CLI utilities, API glue, ops scripts).
- Implement observability (Azure Monitor, Prometheus/Grafana, logs/traces/metrics, alerts, runbooks, SLOs).
- Apply Zero Trust security; Enforce least privilege and role-based access control (RBAC), Identity-based segmentation (Azure AD, Conditional Access, MFA).
- Implement policy-as-code (OPA, Azure Policy) for compliance.
- Rotate secrets and certificates via Key Vault; integrate with pipelines.
- Add continuous security scanning (SAST/DAST, container image scanning).
- Handle reliability: rollout strategies, health probes, incident response, postmortems.
- Optimize costs: right-sizing, autoscaling, budgets, tags, reporting.
Key requirements:
- 4+ years in DevOps/SRE/Platform Engineering.
- Strong Linux (shell, systemd, networking, performance troubleshooting).
- Terraform at scale (modules, state backends, CI/CD integration).
- Deep Azure experience (AKS, VNets, Key Vault, Storage, Monitor, Identity, Networking).
- CI/CD expertise (GitHub Actions and/or Azure DevOps).
- Containers and Kubernetes in production.
- Python or scripting for automation (solid scripting and tooling; not full-time app dev).
- Hands-on with LLM setups (local runners or Azure OpenAI), embeddings, vector indexes, and RAG basics.
Nice to have
- Multi-cloud exposure (AWS / GCP).
- Azure AI services (Azure OpenAI, Cognitive Search).
- GitOps (Argo CD/Flux), Helm packaging, OCI registries.
- Eventing/queues (Event Grid, Service Bus, Kafka).
- Security/compliance in cloud (CIS, NIST, Microsoft CAF).
- Certifications: AZ‑104, AZ‑204, AZ‑400, AI‑900, HashiCorp Terraform Associate, CKA/CKAD.
- Experience with GPU nodes, drivers, CUDA/ROCm, or CPU-only optimizations for LLMs.
How we work
- Everything as code. PRs, reviews, and tests.
- Small batches. Trunk-based or short-lived branches.
- Clear runbooks and on-call rotation where needed.
- Measure, alert, fix, and improve.
Our commitment to diversity & inclusion
We are genuinely passionate about inclusion and we support individuals of all groups; we do not discriminate on the basis of race, religion, gender, sexual orientation, or disability status.
Locations
- Madrid, M, ES, 28003
- Madrid, M, ES 28003
Salary
Estimated Salary Rangemedium confidence
55,000 - 85,000 EUR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Terraformintermediate
- CI/CD (GitHub Actions, Azure DevOps)intermediate
- Kubernetes/AKS (Helm, ingress, autoscaling, node pools, storage)intermediate
- Linux (shell, systemd, networking, performance troubleshooting)intermediate
- Azure (AKS, VNets, Key Vault, Storage, Monitor, Identity, Networking)intermediate
- Containersintermediate
- Python scripting for automationintermediate
- LLM setups (Ollama, vLLM, llama.cpp, Azure OpenAI)intermediate
- Embeddings pipelinesintermediate
- Vector DBs (Azure AI Search/Cognitive Search, pgvector, Milvus)intermediate
- RAG basicsintermediate
- Observability (Azure Monitor, Prometheus/Grafana, logs/traces/metrics, alerts, runbooks, SLOs)intermediate
- Zero Trust securityintermediate
- RBACintermediate
- Azure AD, Conditional Access, MFAintermediate
- Policy-as-code (OPA, Azure Policy)intermediate
- Key Vault for secrets and certificatesintermediate
- Continuous security scanning (SAST/DAST, container image scanning)intermediate
- Rollout strategies, health probes, incident response, postmortemsintermediate
- Cost optimization (right-sizing, autoscaling, budgets, tags, reporting)intermediate
- Everything as codeintermediate
- PRs, reviews, and testsintermediate
- Small batchesintermediate
- Trunk-based or short-lived branchesintermediate
- Clear runbooks and on-call rotationintermediate
- Measure, alert, fix, and improveintermediate
Required Qualifications
- 4+ years in DevOps/SRE/Platform Engineering (experience)
- Strong Linux (shell, systemd, networking, performance troubleshooting) (experience)
- Terraform at scale (modules, state backends, CI/CD integration) (experience)
- Deep Azure experience (AKS, VNets, Key Vault, Storage, Monitor, Identity, Networking) (experience)
- CI/CD expertise (GitHub Actions and/or Azure DevOps) (experience)
- Containers and Kubernetes in production (experience)
- Python or scripting for automation (solid scripting and tooling; not full-time app dev) (experience)
- Hands-on with LLM setups (local runners or Azure OpenAI), embeddings, vector indexes, and RAG basics (experience)
Preferred Qualifications
- Multi-cloud exposure (AWS / GCP) (experience)
- Azure AI services (Azure OpenAI, Cognitive Search) (experience)
- GitOps (Argo CD/Flux), Helm packaging, OCI registries (experience)
- Eventing/queues (Event Grid, Service Bus, Kafka) (experience)
- Security/compliance in cloud (CIS, NIST, Microsoft CAF) (experience)
- Certifications: AZ-104, AZ-204, AZ-400, AI-900, HashiCorp Terraform Associate, CKA/CKAD (experience)
- Experience with GPU nodes, drivers, CUDA/ROCm, or CPU-only optimizations for LLMs (experience)
Responsibilities
- Build and maintain Azure infrastructure with Terraform (modules, workspaces, pipelines, policies)
- Design and operate CI/CD with GitHub Actions and/or Azure DevOps (multi-stage, approvals, environments)
- Run containers and Kubernetes/AKS (Helm, ingress, autoscaling, node pools, storage)
- Manage AI/LLM runtime: local model runners (Ollama, vLLM, llama.cpp), GPU/CPU configs
- Support RAG: embeddings pipelines, vector DBs (Azure AI Search/Cognitive Search, pgvector, Milvus), data sync, retention
- Automate platform tasks with Python (tooling, CLI utilities, API glue, ops scripts)
- Implement observability (Azure Monitor, Prometheus/Grafana, logs/traces/metrics, alerts, runbooks, SLOs)
- Apply Zero Trust security; Enforce least privilege and role-based access control (RBAC), Identity-based segmentation (Azure AD, Conditional Access, MFA)
- Implement policy-as-code (OPA, Azure Policy) for compliance
- Rotate secrets and certificates via Key Vault; integrate with pipelines
- Add continuous security scanning (SAST/DAST, container image scanning)
- Handle reliability: rollout strategies, health probes, incident response, postmortems
- Optimize costs: right-sizing, autoscaling, budgets, tags, reporting
Benefits
- general: Commitment to diversity & inclusion
- general: Support individuals of all groups; no discrimination on the basis of race, religion, gender, sexual orientation, or disability status
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Answer 10 quick questions to check your fit for Senior DevOps Engineer (AI + Azure) @ EY.
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Senior DevOps Engineer (AI + Azure)
EY
Senior DevOps Engineer (AI + Azure)
Job Description
About Us
At EY wavespace Madrid - AI & Data Hub, we are a diverse, multicultural team at the forefront of technological innovation, working with cutting-edge technologies like Gen AI, data analytics, robotics, etc. Our center is dedicated to exploring the future of AI and Data.
Overview:
We’re looking for a Senior DevOps Engineer to build and run cloud and AI infrastructure at scale. You’ll own IaC with Terraform, CI/CD, Kubernetes, and Linux. You’ll also help run LLM workloads both in Azure and locally (Ollama/vLLM/llama.cpp). Your work will enable fast, secure, repeatable delivery.
Key responsibilities
- Build and maintain Azure infrastructure with Terraform (modules, workspaces, pipelines, policies).
- Design and operate CI/CD with GitHub Actions and/or Azure DevOps (multi-stage, approvals, environments).
- Run containers and Kubernetes/AKS (Helm, ingress, autoscaling, node pools, storage).
- Manage AI/LLM runtime: local model runners (Ollama, vLLM, llama.cpp), GPU/CPU configs.
- Support RAG: embeddings pipelines, vector DBs (Azure AI Search/Cognitive Search, pgvector, Milvus), data sync, retention.
- Automate platform tasks with Python (tooling, CLI utilities, API glue, ops scripts).
- Implement observability (Azure Monitor, Prometheus/Grafana, logs/traces/metrics, alerts, runbooks, SLOs).
- Apply Zero Trust security; Enforce least privilege and role-based access control (RBAC), Identity-based segmentation (Azure AD, Conditional Access, MFA).
- Implement policy-as-code (OPA, Azure Policy) for compliance.
- Rotate secrets and certificates via Key Vault; integrate with pipelines.
- Add continuous security scanning (SAST/DAST, container image scanning).
- Handle reliability: rollout strategies, health probes, incident response, postmortems.
- Optimize costs: right-sizing, autoscaling, budgets, tags, reporting.
Key requirements:
- 4+ years in DevOps/SRE/Platform Engineering.
- Strong Linux (shell, systemd, networking, performance troubleshooting).
- Terraform at scale (modules, state backends, CI/CD integration).
- Deep Azure experience (AKS, VNets, Key Vault, Storage, Monitor, Identity, Networking).
- CI/CD expertise (GitHub Actions and/or Azure DevOps).
- Containers and Kubernetes in production.
- Python or scripting for automation (solid scripting and tooling; not full-time app dev).
- Hands-on with LLM setups (local runners or Azure OpenAI), embeddings, vector indexes, and RAG basics.
Nice to have
- Multi-cloud exposure (AWS / GCP).
- Azure AI services (Azure OpenAI, Cognitive Search).
- GitOps (Argo CD/Flux), Helm packaging, OCI registries.
- Eventing/queues (Event Grid, Service Bus, Kafka).
- Security/compliance in cloud (CIS, NIST, Microsoft CAF).
- Certifications: AZ‑104, AZ‑204, AZ‑400, AI‑900, HashiCorp Terraform Associate, CKA/CKAD.
- Experience with GPU nodes, drivers, CUDA/ROCm, or CPU-only optimizations for LLMs.
How we work
- Everything as code. PRs, reviews, and tests.
- Small batches. Trunk-based or short-lived branches.
- Clear runbooks and on-call rotation where needed.
- Measure, alert, fix, and improve.
Our commitment to diversity & inclusion
We are genuinely passionate about inclusion and we support individuals of all groups; we do not discriminate on the basis of race, religion, gender, sexual orientation, or disability status.
Locations
- Madrid, M, ES, 28003
- Madrid, M, ES 28003
Salary
Estimated Salary Rangemedium confidence
55,000 - 85,000 EUR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Terraformintermediate
- CI/CD (GitHub Actions, Azure DevOps)intermediate
- Kubernetes/AKS (Helm, ingress, autoscaling, node pools, storage)intermediate
- Linux (shell, systemd, networking, performance troubleshooting)intermediate
- Azure (AKS, VNets, Key Vault, Storage, Monitor, Identity, Networking)intermediate
- Containersintermediate
- Python scripting for automationintermediate
- LLM setups (Ollama, vLLM, llama.cpp, Azure OpenAI)intermediate
- Embeddings pipelinesintermediate
- Vector DBs (Azure AI Search/Cognitive Search, pgvector, Milvus)intermediate
- RAG basicsintermediate
- Observability (Azure Monitor, Prometheus/Grafana, logs/traces/metrics, alerts, runbooks, SLOs)intermediate
- Zero Trust securityintermediate
- RBACintermediate
- Azure AD, Conditional Access, MFAintermediate
- Policy-as-code (OPA, Azure Policy)intermediate
- Key Vault for secrets and certificatesintermediate
- Continuous security scanning (SAST/DAST, container image scanning)intermediate
- Rollout strategies, health probes, incident response, postmortemsintermediate
- Cost optimization (right-sizing, autoscaling, budgets, tags, reporting)intermediate
- Everything as codeintermediate
- PRs, reviews, and testsintermediate
- Small batchesintermediate
- Trunk-based or short-lived branchesintermediate
- Clear runbooks and on-call rotationintermediate
- Measure, alert, fix, and improveintermediate
Required Qualifications
- 4+ years in DevOps/SRE/Platform Engineering (experience)
- Strong Linux (shell, systemd, networking, performance troubleshooting) (experience)
- Terraform at scale (modules, state backends, CI/CD integration) (experience)
- Deep Azure experience (AKS, VNets, Key Vault, Storage, Monitor, Identity, Networking) (experience)
- CI/CD expertise (GitHub Actions and/or Azure DevOps) (experience)
- Containers and Kubernetes in production (experience)
- Python or scripting for automation (solid scripting and tooling; not full-time app dev) (experience)
- Hands-on with LLM setups (local runners or Azure OpenAI), embeddings, vector indexes, and RAG basics (experience)
Preferred Qualifications
- Multi-cloud exposure (AWS / GCP) (experience)
- Azure AI services (Azure OpenAI, Cognitive Search) (experience)
- GitOps (Argo CD/Flux), Helm packaging, OCI registries (experience)
- Eventing/queues (Event Grid, Service Bus, Kafka) (experience)
- Security/compliance in cloud (CIS, NIST, Microsoft CAF) (experience)
- Certifications: AZ-104, AZ-204, AZ-400, AI-900, HashiCorp Terraform Associate, CKA/CKAD (experience)
- Experience with GPU nodes, drivers, CUDA/ROCm, or CPU-only optimizations for LLMs (experience)
Responsibilities
- Build and maintain Azure infrastructure with Terraform (modules, workspaces, pipelines, policies)
- Design and operate CI/CD with GitHub Actions and/or Azure DevOps (multi-stage, approvals, environments)
- Run containers and Kubernetes/AKS (Helm, ingress, autoscaling, node pools, storage)
- Manage AI/LLM runtime: local model runners (Ollama, vLLM, llama.cpp), GPU/CPU configs
- Support RAG: embeddings pipelines, vector DBs (Azure AI Search/Cognitive Search, pgvector, Milvus), data sync, retention
- Automate platform tasks with Python (tooling, CLI utilities, API glue, ops scripts)
- Implement observability (Azure Monitor, Prometheus/Grafana, logs/traces/metrics, alerts, runbooks, SLOs)
- Apply Zero Trust security; Enforce least privilege and role-based access control (RBAC), Identity-based segmentation (Azure AD, Conditional Access, MFA)
- Implement policy-as-code (OPA, Azure Policy) for compliance
- Rotate secrets and certificates via Key Vault; integrate with pipelines
- Add continuous security scanning (SAST/DAST, container image scanning)
- Handle reliability: rollout strategies, health probes, incident response, postmortems
- Optimize costs: right-sizing, autoscaling, budgets, tags, reporting
Benefits
- general: Commitment to diversity & inclusion
- general: Support individuals of all groups; no discrimination on the basis of race, religion, gender, sexual orientation, or disability status
Tags & Categories
Answer 10 quick questions to check your fit for Senior DevOps Engineer (AI + Azure) @ EY.
10 Questions
~2 Minutes
Instant Score
Related Books and Jobs