Description

You will build and maintain the MLOps and LLMOps platform, owning deployment, monitoring, evaluation, and continuous improvement for AI applications.

This role is on-site.

Responsibilities

• Implement evaluation frameworks to monitor drift, hallucinations, regressions, and compliance.
• Configure and operate observability tools such as Arize AI for metrics and monitoring.
• Develop pipelines for model performance evaluation, data usage drift detection, and guardrail monitoring.
• Implement deployment automation, CI/CD, versioning, and role-based controls for AI workloads.
• Ensure enterprise safety, compliance, and governance standards are met.

Required Skills

• 5+ years in MLOps or platform engineering supporting ML or LLM applications.
• Hands-on experience with AI/LLM observability platforms (e.g., Arize AI).
• Strong understanding of deployment patterns for LLM applications, including agentic and non-agentic frameworks.
• Experience with CI/CD systems, model registries, telemetry logging, and operational monitoring.
• Proficiency with Python engineering.
• Experience with cloud-native services, preferably Azure.

Key Skills

Education

Any Graduate