Description

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

Responsibilities

• Support predictive AI workloads across the entire lifecycle, from ingestion to deployment.
• Troubleshoot issues spanning data ingestion, model inference, and deployment layers.
• Define and implement observability strategies for AI systems in production.
• Manage on-call support for GenAI and predictive pipelines (1 week every 6–8 weeks).
• Maintain understanding of enterprise disaster recovery solutions, including backup and restore processes.

Required Skills

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

Key Skills

Education

Any Gradute