Responsibility :

• Design, implement, and manage continuous integration and continuous deployment (CI/CD) pipelines for machine learning workflows.
• Automate model deployment, scaling, and monitoring in cloud environments (AWS, Azure, GCP).
• Collaborate with data science teams to optimize and streamline the deployment of AI/ML models.
• Implement and manage infrastructure as code (IaC) using tools such as Terraform, Ansible, or CloudFormation.
• Ensure models and services are monitored for performance, reliability, and scalability in production.
• Maintain and improve infrastructure for data pipelines, data storage, and ML model training environments.
• Implement best practices for version control, model tracking, and reproducibility of AI/ML experiments.
• Work on containerization and orchestration technologies (Docker, Kubernetes) for ML deployment.

Requirements :

• 4+ years of hands-on experience in DevOps, infrastructure automation, and cloud technologies, with a focus on AI/ML workflows.
• Experience with machine learning lifecycle management, including model deployment and monitoring.
• Strong proficiency in cloud platforms (AWS, Azure, Google Cloud) and their AI/ML services.
• Experience in containerization technologies (Docker) and container orchestration (Kubernetes).
• Familiarity with machine learning tools and frameworks (TensorFlow, PyTorch, Scikit-learn, etc.).
• Solid understanding of CI/CD tools (Jenkins, GitLab CI, CircleCI) and version control systems (Git).
• Strong scripting skills (Python, Shell, Bash).
• Good understanding of AI/ML model versioning, experimentation tracking, and reproducibility practices.

Preferred Qualifications :

• Experience with serverless architectures for AI/ML applications.
• Familiarity with tools like MLflow, Kubeflow, or Seldon for managing ML workflows..
• Previous experience working on large-scale, production-level AI/ML systems.
• AI/ML certifications or specialized training.

To apply this job, please share your resume at info@softventures.llc