ML Ops Engineer

Job Overview

Job Scope

• Build and maintain ML infrastructure, CI/CD workflows, and simple data pipelines.
• Deploy models into reliable and scalable production environments.
• Monitor model performance, data drift, data pipeline health, and system health.
• Resolve operational issues and provide incident response for production AI services.
• Optimize infrastructure for cost, latency, and scalability.
• Automate retraining, versioning, and release workflows using Databricks and MLflow.
• Reuse enterprise platforms and shared AI capabilities in line with organizational architecture and standards.
• Ensure security, governance, and compliance standards across AI operations.
• Collaborate with AI, Data Engineering, BI, and Enterprise Data teams on shared capabilities and best practices.
• Partner with platform and data engineering teams to align deployment, monitoring, and operational practices.

Job Essentials

• Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, AI, or a related field.
• 3–5 years of experience in MLOps, DevOps, DataOps, or ML/data engineering.
• Proven experience deploying and operating machine learning models in production.
• Practical experience debugging data pipelines and developing simple data pipelines.
• Strong MLOps practices and scalable AI deployment experience.
• Intermediate data engineering skills, including pipeline debugging and simple pipeline development.
• Hands-on experience with Python and preferably Databricks or MLflow.
• Experience with CI/CD, Docker or Kubernetes containerization, and infrastructure as code.
• Experience with model monitoring, observability, drift detection, and data pipeline monitoring.
• Experience with AWS, Azure, or GCP cloud platforms.
• Experience optimizing infrastructure for cost, latency, and scalability.

Desirable Qualifications

• Experience with Databricks and enterprise data platforms is desirable.
• Experience deploying RAG or LLM solutions in production is desirable.