Description

You will own the lifecycle of machine learning models in production environments.

Responsibilities

• Support predictive AI workloads across data ingestion, model inference, and deployment layers.
• Define and implement observability strategies specifically for AI systems.
• Troubleshoot failures in GenAI and predictive pipelines, including on-call rotation.
• Manage CI/CD pipelines and containerization for ML artifacts.
• Implement system-level monitoring and debugging for production ML services.

Required Skills

• 5+ years of professional experience in an engineering capacity.
• Proficiency in Python for automation, data pipelines, and OpenAI integrations.
• Hands-on experience with vector databases (Elasticsearch or similar) and similarity search.
• Strong command-line skills and shell scripting on Linux systems.
• Experience with containerization using Docker and Kubernetes.
• Familiarity with big data platforms such as Hadoop.
• Experience implementing observability using Splunk, Grafana, ELK Stack, or OpenTelemetry.
• Knowledge of CI/CD pipelines and enterprise disaster recovery concepts.

Key Skills

Education

Any Gradute