Responsibilities
- Design scalable, secure architectures and pipelines for building, deploying, and monitoring production ML applications
- Build and enhance MLOps platforms and self-service ML development tooling
- Collaborate with internal teams to enable ML platform adoption and troubleshoot user issues
- Develop CI/CD pipelines, automation, and best practices for model deployment and lifecycle management
- Implement containerization, versioning, monitoring, and drift detection for ML models
Basic qualifications
- Bachelor’s degree with 5+ years of experience, or Master’s degree with 3+ years of experience
- 5+ years of experience with object-oriented programming (Python, Java, Go, C/C++, etc.)
- Hands-on experience with MLOps frameworks such as MLflow or Kubeflow
- Strong proficiency in Python, SQL, and data-driven development
- Experience designing and deploying cloud-based solutions (AWS preferred)
- Experience with CI/CD, DevOps practices, Git-based workflows, and containerization (Docker, Kubernetes)
Preferred qualifications
- Experience building model inference systems and advanced deployment strategies
- Familiarity with tools such as Helm, Helmfile, Terraform, or CloudFormation
- Exposure to ML observability and monitoring tools
- Strong communication skills and ability to translate high-level requirements into actionable tasks
Tags & focus areas
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