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:

- Strong 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.
- Good understanding of cloud-native architectures and software engineering best practices.

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