Design, build, and maintain production CI/CD pipelines and cloud infrastructure to deploy and operate machine learning models at enterprise scale. Implement IaC, containerized workloads, monitoring, and automation in AWS (SageMaker emphasis), collaborate with cross-functional teams, and mentor junior engineers to improve platform reliability and operational maturity.
Callibrity is based in Cincinnati, Ohio. This role may be virtual, remote based but must work U.S. Eastern Time work hours. Strongly prefer candidates in Midwestern and Eastern / Southeastern U.S. cities. Will not consider non-US based candidates.
Callibrity is a developer owned and managed custom software development consulting company that is dedicated to creating quality software using modern technologies and adding unquestionable business value to companies across multiple industries and verticals. We are problem solvers...people who like a challenge and enjoy working with modern tech stacks. We offer an incredibly collaborative culture and enjoy solving complex problems with our clients.
At Callibrity, we partner with organizations to solve complex software engineering challenges through modern technology, thoughtful collaboration, and exceptional engineering talent. We're looking for a Senior DevOps Engineer to join a high-impact engagement where you'll help operationalize machine learning at enterprise scale. This is an opportunity to work alongside an experienced architecture team while building the deployment pipelines, cloud infrastructure, and operational capabilities that bring machine learning models into production.
As a Senior DevOps Engineer, you'll focus on the engineering and operational side of machine learning—not model development. You'll work closely with software engineers, data engineers, architects, and business stakeholders to build reliable, scalable deployment pipelines and cloud infrastructure.
Key Responsibilities
- Design, build, and maintain production-grade CI/CD pipelines for machine learning applications and services.
- Deploy and operationalize machine learning models using AWS services, with an emphasis on Amazon SageMaker.
- Develop and maintain production Python applications supporting model deployment, inference, automation, and platform tooling.
- Build and deploy cloud infrastructure using Infrastructure as Code (Terraform or AWS CloudFormation).
- Deploy and support containerized workloads within AWS-based machine learning environments.
- Create automated deployment, testing, monitoring, and promotion processes across multiple environments.
- Collaborate with architects and engineering teams to improve platform reliability, scalability, and operational maturity.
- Implement monitoring, alerting, logging, and operational best practices to support production systems.
- Mentor junior engineers and promote engineering best practices across the team.
- Partner with cross-functional teams including platform engineering, infrastructure, security, and business stakeholders.
Qualifications
- 7+ years of software engineering, DevOps, platform engineering, or MLOps experience.
- Strong production-level Python development experience.
- Deep experience building, testing, and promoting applications through CI/CD pipelines (tool agnostic).
- Experience deploying containerized applications in cloud environments.
- Hands-on Infrastructure as Code experience using Terraform or AWS CloudFormation.
- Strong AWS experience deploying production workloads.
- Experience supporting the machine learning deployment lifecycle, including model deployment, inference pipelines, monitoring, and operational support.
- Ability to contribute quickly with minimal ramp-up time.
- Excellent communication skills with the ability to collaborate across technical and non-technical teams.
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