We are looking for an AI Observability (MLOps) Engineer to build and maintain scalable, cloud-native solutions that support AI/ML applications. The ideal candidate should have robust experience in MLOps, DevOps, cloud infrastructure, and machine learning operations.

Key Responsibilities:
Build and standardize AI/ML observability using logging, metrics, tracing, and model performance monitoring.
Implement DevOps, MLOps, and DataOps best practices with Infrastructure as Code (IaC) and automation.
Design and maintain data pipelines with automated data quality monitoring and anomaly detection.
Deploy, monitor, and manage containerized ML workloads.
Develop and enhance CI/CD pipelines for ML workflows and infrastructure.
Implement model monitoring, drift detection, bias detection, explainability, and governance.
Develop automated test suites for model performance, regression, edge cases, and bias validation.




Monitor model KPIs such as accuracy, precision, recall, latency, and calibration.
Ensure reproducibility of experiments and production models.
Collaborate with engineering teams to create reusable MLOps frameworks, documentation, and best practices.

Required Skills:
Robust experience with Python.
Hands-on experience with Azure or another major cloud platform.
Experience with MLOps, DevOps, CI/CD, Infrastructure as Code (IaC), and containerization (Docker/Kubernetes).
Knowledge of TensorFlow, PyTorch, or Scikit-learn.
Experience with data pipelines, model deployment, monitoring, and AI observability.
Positive understanding of cloud-native architectures and software engineering best practices.

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