Senior MLOps Engineer - SRE | DevOps

Jobgether • Brazil

Remote

This Job is No Longer Active This position is no longer accepting applications

AI Summary

This role is responsible for ensuring machine learning and LLM systems run reliably, efficiently, and at scale in production environments. The Senior MLOps Engineer will operate at the intersection of infrastructure, DevOps, and machine learning, owning the lifecycle from model deployment to production-grade inference services. The ideal candidate will have 5+ years of experience in Platform Engineering, SRE, DevOps, or MLOps roles, operating production systems at scale.

Key Highlights

Design, build, and operate scalable ML and inference infrastructure

Own the end-to-end ML deployment lifecycle

Operate and optimize production-grade AI and LLM workloads

Key Responsibilities

Design, build, and operate scalable ML and inference infrastructure supporting real-time and batch workloads across multiple tenants

Own the end-to-end ML deployment lifecycle, including model registry, versioning, rollout strategies, and safe rollback mechanisms

Operate and optimize production-grade AI and LLM workloads, managing inference providers, throttling, quotas, and fallback strategies under load

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Technical Skills Required

Terraform GitOps Kubernetes AWS GPU scheduling Infrastructure-as-Code

Benefits & Perks

Fully remote work model with flexibility

Opportunity to work on cutting-edge AI and ML infrastructure at scale

High ownership environment with direct impact on platform architecture and evolution

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Nice to Have

Experience with GPU/accelerator scheduling and node lifecycle management

Experience operating LLM inference systems at scale

Experience with ML orchestration tools such as Argo Workflows, Kubeflow, Airflow, or SageMaker Pipelines

Job Description

This role sits at the core of a modern AI/ML platform, responsible for ensuring that machine learning and LLM systems run reliably, efficiently, and at scale in production environments.You will operate at the intersection of infrastructure, DevOps, and machine learning, owning the lifecycle from model deployment to production-grade inference services.The position involves solving complex engineering challenges such as latency optimization, autoscaling, cost efficiency, and reliability of AI workloads.You will help define how ML systems are built and operated, introducing strong SRE practices to environments that demand high availability and performance.

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