Senior DevOps Engineer
About The Position
Callibrity is a developer-owned and managed custom software development consulting company dedicated to creating quality software using modern technologies and adding unquestionable business value. We are problem solvers who enjoy challenges and modern tech stacks, offering a collaborative culture for solving complex problems with clients. We partner with organizations to solve complex software engineering challenges through modern technology, thoughtful collaboration, and exceptional engineering talent. We are looking for a Senior DevOps Engineer to join a high-impact engagement where you will help operationalize machine learning at enterprise scale. This role offers the chance to work with an experienced architecture team while building deployment pipelines, cloud infrastructure, and operational capabilities to bring machine learning models into production. As a Senior DevOps Engineer, your focus will be on the engineering and operational aspects of machine learning, not model development. You will collaborate closely with software engineers, data engineers, architects, and business stakeholders to build reliable, scalable deployment pipelines and cloud infrastructure.
Requirements
- 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.
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.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
