
Machine Learning Deployment Specialist
6 days ago
We are seeking a passionate Machine Learning Deployment Engineer to drive deployment, monitoring, and optimization of machine learning models in production. This role ensures the reliability, scalability, and efficiency of our ML infrastructure while supporting AI-driven solutions development.
Key Responsibilities:- Design, build, and maintain scalable ML deployment pipelines for real-time and batch inference.
- Manage and optimize cloud-based ML infrastructure for high availability and cost efficiency.
- Implement monitoring, logging, and alerting systems to track model performance and data drift.
- Automate model training, evaluation, and deployment using CI/CD pipelines.
- Ensure compliance with MLOps best practices, including versioning and governance.
- Collaborate with data scientists and engineers to transition models into production.
- Hands-on experience with MLOps platforms (e.g., MLflow, Kubeflow, SageMaker).
- Strong expertise in cloud services (AWS, GCP, Azure).
- Proficiency in containerization (Docker, Kubernetes) and infrastructure as code (Terraform, CloudFormation).
- Experience in building CI/CD pipelines for ML models.
- Programming skills in Python, Go, or Shell scripting.
- Familiarity with data versioning and monitoring tools (DVC, Prometheus).
- Understanding of feature stores and data management for ML workflows.
- Work with cutting-edge MLOps and AI deployment technologies in a growing industry.
- Be part of a dynamic, innovative team focused on AI and cloud solutions.
- Gain exposure to end-to-end ML workflows.
- Opportunities for professional growth in cloud computing, automation, and AI infrastructure.
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