Description

You will own the engineering and operational lifecycle of machine learning systems in production.

This role is on-site.

Responsibilities

• Support predictive AI and GenAI workloads running in production environments.
• Troubleshoot issues spanning data ingestion, model inference, and deployment layers.
• Define and implement observability strategies for AI systems.
• Manage on-call rotation for GenAI and predictive pipelines.
• Ensure execution of enterprise disaster recovery procedures.

Required Skills

• 5+ years of professional experience in software engineering or MLOps.
• Proficiency in Python for automation scripting and building data pipelines.
• Experience with containerization using Docker and Kubernetes.
• Familiarity with observability tools: Splunk, Grafana, ELK Stack, and OpenTelemetry.
• Hands-on experience with vector databases (Elasticsearch or similar) and similarity search.
• Strong command-line skills and shell scripting in Linux environments.
• Familiarity with CI/CD pipelines and AI model deployment.

Preferred Skills

• Exposure to GCP and Azure cloud environments.
• Experience managing the full Data Science Lifecycle.

Key Skills

Education

Any Gradute