Description

You will own the engineering and operational aspects of production ML systems.

This role is on-site.

Responsibilities

• Support predictive AI workloads in production, troubleshooting data ingestion, model inference, and deployment layers.
• Define and implement observability strategies for AI systems, proactively monitoring model failures and latency.
• Manage and maintain CI/CD pipelines to ensure smooth deployment of ML services.
• Handle on-call support for GenAI and predictive pipelines on a rotation schedule.
• Implement and maintain system health monitoring using established observability tools.

Required Skills

• 10+ years of professional experience in software or ML engineering.
• Proficiency in Python for automation scripting and building data pipelines.
• Experience with containerization using Docker and Kubernetes.
• Strong command-line skills and shell scripting proficiency on Linux systems.
• Hands-on experience with Elasticsearch or similar vector databases, including similarity search.
• Experience with monitoring tools such as Splunk, Grafana, ELK stack, or OpenTelemetry.
• Familiarity with CI/CD pipelines.
• Experience with Hadoop-based platforms (MapR, Hortonworks).
• Understanding of enterprise disaster recovery (DR) solutions.

Preferred Skills

• Exposure to GCP and Azure environments.
• Experience with full-cycle data science projects from problem definition through operationalization.

Key Skills

Education

Any Graduate