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# Senior DevOps/MLOps Engineer

- **Company:** Algorized Inc
- **Location:** Campbell, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Bash Shell, C++ (Programming Language), Cloud Engineering, Computer Programming, Databases, Continuous Integration, DevOps, Github, Identity and Access Management, Python (Programming Language), Machine Learning, Azure Machine Learning, Systems Integration, Data Storage Technologies, Cloud Platform System, Delivery Pipeline, Cloudformation, Containerization, Gitlab-ci, Kubernetes, Information Technology, Machine Learning Operations, Terraform, Software Version Control, Docker, Jenkins
- **Published:** June 6, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=0c88a8d02f220104

## About the Role

Do you have experience in Writing skills?, * MSc in Computer Science, Engineering, or a relevant field (or equivalent practical experience) with 5+ years of experience in DevOps, Cloud Engineering, or MLOps.

 * Deep, hands-on expertise with AWS services (EC2, S3, IAM, ECR, ECS/EKS, SageMaker).

 * Strong programming proficiency in Python and Bash, combined with working knowledge/experience in C/C++ to collaborate effectively with our embedded engineering teams.

 * Strong proficiency in writing Infrastructure as Code (Terraform, CloudFormation, or equivalent).

 * Proven experience designing and maintaining CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, or similar).

 * Extensive experience with containerized environments (Docker) and container orchestration (Kubernetes/EKS).

 * Practical experience supporting machine learning deployment workflows and model serving.

 * Strong problem-solving skills with the ability to document systems and infrastructure clearly.

## Description

* AWS & ML Infrastructure: Build, own, and scale the end-to-end AWS cloud infrastructure (including compute, container orchestration, and provisioning databases for both real-time serving and large-scale ML data storage).

 * MLOps Pipelines: Provide and maintain tooling, templates, and best practices for ML workflows, including model versioning, automated training pipelines, and serving endpoints (e.g., using SageMaker).

 * CI/CD & Automation: Create and manage comprehensive CI/CD pipelines to support fast, reliable deployments of our cloud platform and ML services.

 * System Integrations: Write integration code and APIs to seamlessly connect our ML cloud environments with customer systems and edge/embedded devices.

 * Monitoring & Reliability: Monitor, troubleshoot, and continuously improve production systems with a strict focus on system performance, security, and AWS cost-optimization.

 * Cross-Functional Collaboration: Actively participate in the integration of real-time solutions, working closely with data scientists and embedded engineers to deliver on customer needs.


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