DevOps/MLOps Engineer (ML / LLM Infrastructure)

Descrição da Vaga

Requisitos

• Experience: 4+ years in DevOps, Platform Engineering, or ML Infrastructure roles, with strong understanding of production systems and distributed workloads
• Strong understanding of cloud-native architectures and experience designing scalable systems for compute and data-intensive workloads
• Kubernetes & Containers: Solid experience with Docker and Kubernetes (preferably GKE), including deploying, scaling, and operating production workloads
• Familiarity with Helm and Kubernetes networking fundamentals
• CI/CD & Automation: Experience building and maintaining CI/CD pipelines (GitHub Actions, Jenkins, or similar) to automate testing, deployment, and infrastructure changes
• Workflow Orchestration: Experience with Airflow (or similar tools)
• Programming: Strong hands-on scripting languages experience (Bash and/ or Python)
• Observability & Reliability: Experience with monitoring and logging systems (e.g., Prometheus, Grafana)
• Understanding of reliability, alerting, and debugging in distributed systems

Responsabilidades

• You will be responsible for keeping our ML infrastructure reliable, scalable, and efficient - from data pipelines to training and inference
• In this role, you will develop and maintain CI/CD pipelines, orchestration workflows, and observability for distributed ML workloads across GPU/TPU/CPU environments
• This is a DevOps-first role with strong exposure to ML infrastructure
• You will work closely with ML Engineers and Data Engineers, while focusing on building a robust, automated, and production-grade platform that accelerates model development and delivery
• Design, build, and operate scalable ML infrastructure on GCP (GKE), supporting both experimentation and production workloads for LLMs and NLP systems
• Manage Kubernetes-based environments (GKE): deployment, scaling, upgrades, and reliability of training and inference workloads across GPU/TPU/CPU pools
• Build and maintain CI/CD pipelines (GitHub Actions, Jenkins) to automate testing, training, and deployment of ML services and infrastructure
• Implement infrastructure as code (Terraform, Ansible) to provision and manage cloud resources in a reproducible, secure, and cost-efficient way
• Ensure observability of ML systems: monitoring, logging, and alerting for infrastructure, pipelines, and production inference workloads
• Collaborate with ML engineers and Data Engineers to design and support reliable training and inference pipelines
• Optimize resource utilization and cost, improving efficiency of training and serving infrastructure
• Troubleshoot and resolve issues across the ML platform - from data pipelines to distributed training and production deployments
• Contribute to engineering best practices: code reviews, automation, and continuous improvement of platform reliability and developer experience
• ML Infrastructure Understanding: Familiarity with the ML lifecycle (training, evaluation, inference) and experience supporting ML workloads in production environments
• Collaboration: Ability to work closely with ML Engineers and Data Engineers, translating ML requirements into reliable and scalable infrastructure solutions

Benefícios