Description

You will own the end-to-end MLOps lifecycle for production AI systems.

Responsibilities

• Support predictive AI workloads from ingestion to deployment.
• Troubleshoot issues across data ingestion, model inference, and deployment layers.
• Define and implement observability strategies for production AI systems.
• Manage on-call support for GenAI and predictive pipelines.
• Maintain enterprise disaster recovery solutions, including backup and restore processes.

Required Skills

• 5+ years of professional engineering experience.
• Proficiency in Python for automation scripting and AI data pipelines.
• Hands-on experience with Docker and Kubernetes containerization.
• Experience implementing CI/CD pipelines.
• Familiarity with observability tools: Splunk, Grafana, ELK Stack, or OpenTelemetry.
• Experience with big data technologies: Hadoop or MapR/Hortonworks.
• Strong command-line skills and shell scripting for Linux.
• Experience with vector databases, including similarity search and embedding management.

Key Skills

Education

Any Gradute