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Tech Stack
Tools & technologiesCloudDockerKubernetes
About the role
Key responsibilities & impact- train, test, deploy, and maintain models that learn from data
- lead the direction of critical solutions by applying best-fit ML algorithms
- collaborate with AI software engineers, DevSecOps, and Data Engineers
- guide clients as they navigate the landscape of ML algorithms, tools, and frameworks
Requirements
What you’ll need- Experience deploying production grade ML models onto cloud environments
- Experience in managing ML workloads to design elastic infrastructure
- Knowledge of MLOps Frameworks and Container Systems, such as Kubernetes or Docker
- Bachelor's degree in Computer Science or Software Engineering
Benefits
Comp & perks- health, life, disability, financial, and retirement benefits
- paid leave
- professional development
- tuition assistance
- work-life programs
- dependent care awards for exceptional performance
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningMLOpscloud environmentsproduction grade modelselastic infrastructureKubernetesDocker
Soft Skills
collaborationleadershipguidance
Certifications
Bachelor's degree in Computer ScienceBachelor's degree in Software Engineering
