MLOps / ML Platform Engineer

Remote — United States | Full-timeWe’re looking for a hands-on MLOps / ML Platform Engineer to build, support, and improve production infrastructure used by Machine Learning and Data Science teams.This role sits between DevOps, cloud infrastructure, and MLOps. You’ll work with AWS and Kubernetes infrastructure while helping ML teams deploy, serve, scale, monitor, and operate models reliably in production.What you’ll doBuild, operate, and improve production cloud infrastructure on AWSSupport Kubernetes-based workloads, particularly Amazon EKSProvision and manage infrastructure using Terraform, Terragrunt, CloudFormation, or similar IaC toolsWork closely with ML Engineers and Data Scientists to support production ML workloadsBuild and support infrastructure for ML pipelines, model deployment, model serving, and inferenceOperate MLOps platforms such as Databricks, SageMaker, MLflow, or similar technologiesSupport Databricks clusters, jobs, pipelines, compute, and model-serving workloadsSupport scalable inference infrastructure, including compute capacity, autoscaling, and production reliabilityMonitor ML infrastructure and production workloads, including serving health, latency, resource utilization, and related ML/model metricsBuild and maintain CI/CD workflows for application and ML model deployments using tools such as GitLab CI/CD, Jenkins, or GitHub ActionsAutomate infrastructure and operational processes using Python and BashMaintain monitoring and observability using Prometheus, Grafana, CloudWatch, Coralogix, or similar toolsTroubleshoot production infrastructure, deployment, ML platform, and serving issues and participate in RCAApply cloud security and DevSecOps practices across infrastructure and deployment workflowsHelp improve infrastructure supporting newer AI use cases, including LLMs, AI agents, MCP integrations, and AI-assisted engineering workflowsWhat we’re looking for3+ years of experience in DevOps, MLOps, Platform Engineering, SRE, Cloud Engineering, or a similar infrastructure-focused roleStrong hands-on experience with AWSProduction experience with Kubernetes/EKSHands-on Infrastructure as Code experience with Terraform or similar toolingProduction experience supporting ML/MLOps infrastructureExperience working directly with MLOps, ML Engineering, or Data Science teamsHands-on experience with at least one MLOps platform such as Databricks, SageMaker, MLflow, or KubeflowExperience supporting ML pipelines and model deploymentExperience with model serving and/or inference infrastructure in productionExperience monitoring and troubleshooting production ML infrastructure and workloadsCI/CD experience with Jenkins, GitLab CI/CD, GitHub Actions, or similar toolsPython and/or Bash scriptingStrong Linux, networking, observability, and production troubleshooting skillsNice to haveDatabricks model serving and ML pipelinesSageMaker endpoints and ML pipelinesMLflow Model Registry and experiment trackingGPU-based Kubernetes workloadsAutoscaling for high-throughput inferenceModel drift, data quality, inference performance, or model-specific monitoringHelm, ArgoCD, GitOps, or service mesh technologiesServerless/event-driven AWS infrastructure such as Lambda, API Gateway, and SQSMulti-cloud experienceLLMOps, RAG, AI agents, MCP, or other agentic AI infrastructureFinOps and cloud cost optimizationCloud security and DevSecOps experienceCandidates must be authorized to work in the United States without requiring employer visa sponsorship now or in the future.