Machine Learning Specialist

Job details

• Role: ML Ops Engineer
• Years of Experience Required: 1 to 6 years
• CTC: 1 to 3 years: 7 LPA to 10 LPA & 4 to 6 years: 10 LPA to 15 LPA

Job Description

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 strong 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.