Senior MLOps Engineer

Salvo Software

. Design, deploy, and operate MLOps platform infrastructure, cloud services (AWS, Azure, GCP), and LLM inference pipelines (Ollama, vLLM).

Posted 11/6/2026full-timeRemote • MexicoSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying MLOps platform infrastructure and CI/CD pipelines, with a strong focus on cloud services and ML model deployment. Proficient in managing Linux servers, networking, and security while establishing best practices for DevOps standards.

Highest-signal resume keywords

MLOps Platform InfrastructureCI/CD Pipeline DevelopmentInfrastructure as Code (Terraform, Ansible)Linux Server ManagementCloud Services (AWS, Azure, GCP)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

DevOpsSREML Model DeploymentLLM Inference EnginesDockerKubernetesBash ScriptingPython ScriptingCore NetworkingMonitoring/Logging

Tools & Technologies

GitHub ActionsAzure DevOpsPrometheusGrafanaProxmoxVMwareCUDANVIDIAMLflowKubeflow

Industry Keywords

Infrastructure TemplatesProduction ReadinessRoot Cause AnalysisZero TrustSOC 2

Tech Stack

Tools & technologies

AirflowAnsibleAWSAzureCloudDNSDockerFirewallsGoogle Cloud PlatformGrafanaKafkaKubernetesLinuxPrometheusPythonTerraformVMware

About the role

Key responsibilities & impact

Design, deploy, and operate MLOps platform infrastructure, cloud services (AWS, Azure, GCP), and LLM inference pipelines (Ollama, vLLM).
Build automated CI/CD pipelines, Infrastructure as Code, and monitoring/alerting systems for RAG applications, AI agents, and production deployments.
Manage Linux servers, networking, security endpoints, and troubleshoot production issues with AI engineering teams.
Establish department-wide MLOps/DevOps standards, best practices, and reusable infrastructure templates.
Guide AI Developers and RAG Engineers on architecture, deployment strategies, tool selection, and production readiness.
Translate feature requirements into reliable, secure, cost-effective infrastructure and maintain comprehensive system documentation.
Provision containerized workloads, Linux servers, and infrastructure using Terraform and Ansible across cloud and on-prem environments.
Manage CI/CD with GitHub Actions and Azure DevOps, observability with Prometheus and Grafana, secure networking, and GPU-enabled hardware.
Troubleshoot platform incidents, perform root cause analysis, and maintain operational resilience.

Requirements

What you’ll need

5+ years of senior-level experience in DevOps/SRE/platform engineering with at least 1 year deploying ML models and LLM inference engines (Ollama, vLLM) in production.
Proficiency with Linux, Docker, Kubernetes, virtualization (Proxmox, VMware), GPU infrastructure (CUDA/NVIDIA), and major cloud platforms (AWS, Azure, GCP).
Hands-on expertise with Infrastructure as Code (Terraform, Ansible), CI/CD platforms (GitHub Actions, Azure DevOps), Bash, and Python scripting.
Solid background in core networking (DNS, TLS, firewalls, VPNs), monitoring/logging, and security/authentication (OAuth2/OIDC, API gateways, Zero Trust/SOC 2).
Familiarity with ML tooling (MLflow, Kubeflow), vector DBs, RAG/agent frameworks (MCP), workflow orchestration (Airflow/Kafka), and model optimization/quantization.