Key Responsibilities:
AI/ML Pipeline Management
- Design, implement, and manage AI/ML pipelines for model deployment, monitoring, and performance tuning.
- Collaborate with data scientists and software engineers to integrate machine learning models into production systems.
- Automate deployment, monitoring, and maintenance processes to improve productivity and efficiency.
Infrastructure & Cloud Management
- Manage cloud-based (AWS, Azure) and on-premises infrastructure supporting AI/ML operations.
- Apply best practices for containerization (Docker) and orchestration (Kubernetes).
- Implement continuous integration and deployment (CI/CD) pipelines using Jenkins, GitHub, UrbanDeploy, or similar tools.
Security & Compliance
- Conduct reviews and audits to ensure compliance with industry standards and regulatory requirements.
- Work with security tools like SONAR, Checkmarx, Nexus, Rapid7, DAST to ensure code and infrastructure security.
- Follow best practices for data privacy, security, and governance.
Support & Troubleshooting
- Troubleshoot and resolve issues related to AI/ML applications, APIs, and services.
- Handle database deployments and manage code deployments for Java and Python applications.
