ML Ops Engineer

Responsibilities

• Design, implement, and manage end-to-end ML pipelines for model training, testing, and deployment
• Deploy and maintain machine learning models in production environments
• Develop and optimize data pipelines using SQL (mandatory)
• Work closely with Data Scientists and Data Engineers to operationalize ML models
• Build and maintain CI/CD pipelines for ML workflows
• Monitor model performance and ensure scalability and reliability
• Utilize Databricks for data engineering, model development, and deployment
• Ensure best practices in data governance, versioning, and reproducibility
• Troubleshoot and resolve production issues efficiently
• Strong experience in ML Ops (Model Deployment, Monitoring, CI/CD)
• Hands-on experience with Databricks
• Strong SQL expertise (mandatory) for data manipulation and pipeline development
• Good communication and stakeholder management skills
• Experience with cloud platforms (AWS/Azure/GCP)
• Bachelor's or Master's degree in Computer Science, Engineering, or related field
• 4+ years of relevant experience in ML Ops / Data Engineering / ML Engineering

Additional Information

We are looking for a skilled ML Ops Engineer with strong experience in deploying and managing machine learning models in production environments. The ideal candidate must have hands-on expertise in ML Ops practices , Databricks , and strong SQL skills (mandatory) along with good communication abilities to collaborate effectively with cross-functional teams.

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Company Intel

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