Description

You will build and maintain the MLOps / LLMOps platform that enables deployment, monitoring, evaluation, and continuous improvement of AI/LLM applications.

Responsibilities

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

Required Skills

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

Key Skills

Education

Any Graduate