MLOPS Engineer, Computer Vision
Schwarz Corporate Solutions
full-time
Posted on:
Location Type: Office
Location: Bucharest • Romania
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Tech Stack
About the role
- Build & Maintain ML Infrastructure: Design, implement, and maintain our cloud-based infrastructure for large-scale computer vision model training and data management.
- Automate ML Pipelines: Engineer and deploy automated, production-grade ML pipelines for seamless data processing, model training, validation, and deployment.
- Enable AI/ML Teams: Collaborate directly with Data Scientists and AI Engineers to streamline and accelerate the entire model development lifecycle.
- Ensure Scalability & Reliability: Architect and operate robust, secure, and efficient infrastructure for our large-scale AI solutions.
Requirements
- Production MLOps Experience: Strong, relevant work experience operating and scaling machine learning systems and AI workflows in a production environment.
- Kubernetes Mastery: Deep, hands-on proficiency with Kubernetes for scheduling and scaling ML training jobs and complex workloads.
- ML Pipeline Expertise: Proven ability to build, manage, and troubleshoot ML pipelines and serving infrastructure. Direct experience with Argo Workflows and ArgoCD is an advantage.
- MLOps Tooling: Proficiency with modern MLOps tools, especially MLFlow for experiment tracking and model management.
- Infrastructure as Code (IaC): Solid practical experience managing cloud infrastructure using Terraform.
- Pragmatic Problem-Solver: Demonstrated ability to quickly and independently solve complex technical challenges with reliable, scalable solutions.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
machine learningcomputer visionMLOpsKubernetesML pipelinesArgo WorkflowsArgoCDMLFlowInfrastructure as CodeTerraform
Soft skills
collaborationproblem-solvingindependencetechnical challenges