Senior MLOps Engineer

This is a fully remote position, open to applicants in Mexico.

📋 Description

• Design, implement, and manage MLOps platform infrastructure, cloud services (AWS, Azure, GCP), and LLM inference pipelines (Ollama, vLLM).

• Develop automated CI/CD pipelines, Infrastructure as Code, and monitoring/alerting systems for RAG applications, AI agents, and production deployments.

• Oversee Linux servers, networking, security endpoints, and resolve production issues in collaboration with AI engineering teams.

• Set department-wide MLOps/DevOps standards, best practices, and reusable infrastructure templates.

• Provide guidance to AI Developers and RAG Engineers on architecture, deployment strategies, tool selection, and production readiness.

• Convert feature requirements into dependable, secure, cost-efficient infrastructure and maintain thorough system documentation.

• Provision containerized workloads, Linux servers, and infrastructure utilizing Terraform and Ansible across both 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.

• Address platform incidents, conduct root cause analysis, and ensure operational resilience.

⛳️ Requirements

• 5+ years of advanced-level experience in DevOps/SRE/platform engineering, with at least 1 year of experience deploying ML models and LLM inference engines (Ollama, vLLM) in production.

• Proficient in Linux, Docker, Kubernetes, virtualization (Proxmox, VMware), GPU infrastructure (CUDA/NVIDIA), and major cloud platforms (AWS, Azure, GCP).

• Hands-on experience with Infrastructure as Code (Terraform, Ansible), CI/CD platforms (GitHub Actions, Azure DevOps), Bash, and Python scripting.

• Strong foundation in core networking (DNS, TLS, firewalls, VPNs), monitoring/logging, and security/authentication (OAuth2/OIDC, API gateways, Zero Trust/SOC 2).

• Knowledge of ML tooling (MLflow, Kubeflow), vector databases, RAG/agent frameworks (MCP), workflow orchestration (Airflow/Kafka), and model optimization/quantization.

🏝️ Benefits

• Competitive salary and performance-based incentives.

• Opportunities for professional development and continuous learning.

• Flexible work arrangements and a supportive team environment.