MLOps / ML Platform Engineer
Remote: United States | Full-time
We’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 do:
- Build, operate, and improve production cloud infrastructure on AWSSupport Kubernetes-based workloads, particularly Amazon EKSProvision and manage infrastructure using Terraform, Terragrunt, Cloud
- Formation, or similar IaC tools
- Work closely with ML Engineers and Data Scientists to support production ML workloads
- Build and support infrastructure for ML pipelines, model deployment, model serving, and inference
- Operate MLOps platforms such as Databricks, SageMaker, MLflow, or similar technologies
- Support Databricks clusters, jobs, pipelines, compute, and model-serving workloads
- Support scalable inference infrastructure, including compute capacity, autoscaling, and production reliability
- Monitor ML infrastructure and production workloads, including serving health, latency, resource utilization, and related ML/model metrics
- Build and maintain CI/CD workflows for application and ML model deployments using tools such as GitLab CI/CD, Jenkins, or GitHub Actions
- Automate infrastructure and operational processes using Python and Bash
- Maintain monitoring and observability using Prometheus, Grafana, CloudWatch, Coralogix, or similar tools
- Troubleshoot production infrastructure, deployment, ML platform, and serving issues and participate in RCAApply cloud security and DevSecOps practices across infrastructure and deployment workflows
- Help improve infrastructure supporting newer AI use cases, including LLMs, AI agents, MCP integrations, and AI-assisted engineering workflows
What we’re looking for:
- 3+ years of experience in DevOps, MLOps, Platform Engineering, SRE, Cloud Engineering, or a similar infrastructure-focused role
- Strong hands-on experience with AWSProduction experience with Kubernetes/EKSHands-on Infrastructure as Code experience with Terraform or similar tooling
- Production experience supporting ML/MLOps infrastructure
- Experience working directly with MLOps, ML Engineering, or Data Science teams
- Hands-on experience with at least one MLOps platform such as Databricks, SageMaker, MLflow, or Kubeflow
- Experience supporting ML pipelines and model deployment
- Experience with model serving and/or inference infrastructure in production
- Experience monitoring and troubleshooting production ML infrastructure and workloadsCI/CD experience with Jenkins, GitLab CI/CD, GitHub Actions, or similar tools
- Python and/or Bash scripting
- Strong Linux, networking, observability, and production troubleshooting skills
Nice to have:
- Databricks model serving and ML pipelines
- SageMaker endpoints and ML pipelinesMLflow Model Registry and experiment trackingGPU-based Kubernetes workloads
- Autoscaling for high-throughput inference
- Model drift, data quality, inference performance, or model-specific monitoring
- Helm, ArgoCD, GitOps, or service mesh technologies
- Serverless/event-driven AWS infrastructure such as Lambda, API Gateway, and SQSMulti-cloud experienceLLMOps, RAG, AI agents, MCP, or other agentic AI infrastructure
- FinOps and cloud cost optimization
- Cloud security and DevSecOps experience
Candidates must be authorized to work in the United States without requiring employer visa sponsorship now or in the future.