About the Role 

As a MLOps Engineer within the SN AI Lab, you will join a team of engineers designing, deploying, and scaling innovative AI solutions in cloud environments. You will play a critical role in bridging machine learning development and production operations, ensuring AI systems are reliable, secure, scalable, and aligned with business needs. 

Working in a fast-paced and collaborative environment, you will contribute to the end-to-end delivery of AI products, driving engineering excellence, operational efficiency, and responsible AI practices across the organization. 

Role Responsibilities: 

• Design, build, and maintain scalable MLOps platforms, frameworks, and deployment pipelines that support the reliable delivery of machine learning and generative AI solutions.  
• Develop and integrate cloud-native services, automation workflows, and CI/CD practices to improve operational efficiency, system reliability, and deployment velocity.  
• Implement monitoring, observability, tracing, and performance management capabilities to proactively identify issues and optimize production systems.  
• Drive process optimization initiatives that improve model lifecycle management, operational resilience, governance, and overall team effectiveness.  
• Contribute to data security, compliance, and governance standards by ensuring appropriate controls, monitoring, and responsible management of AI and data assets.  
• Support knowledge sharing, technical storytelling, and documentation to increase transparency, adoption, and understanding of AI capabilities across the organization.  
• Mentor and support junior engineers, helping develop MLOps and AI engineering capabilities across the team.  
• Stay current with emerging technologies, AI engineering practices, and industry trends, identifying opportunities to enhance our platforms and delivery approaches.  

About You:

• A degree in Software Engineering, Computer Science, Artificial Intelligence, or a related technical field.  
• Strong experience developing software solutions using Python and modern engineering practices.  
• Experience with GitHub, Docker, and machine learning frameworks such as PyTorch or TensorFlow.  
• Hands-on experience working with cloud platforms such as Azure, AWS, or GCP.  
• Experience building APIs and services using FastAPI or similar frameworks. 
• Knowledge of CI/CD pipelines, automated testing frameworks, and GitHub Actions.  
• Experience implementing observability, monitoring, and tracing solutions for AI and machine learning applications, including tools such as Langfuse or equivalent platforms.  
• Understanding of machine learning lifecycle management, deployment strategies, and production monitoring.  
• Experience deploying and monitoring AI agents and LLM-based applications is considered an advantage. 

Having a good command of English is important; collaboration is important in our day to day work, so being able to communicate your ideas and understand others’ is key.

About the Role 

As a MLOps Engineer within the SN AI Lab, you will join a team of engineers designing, deploying, and scaling innovative AI solutions in cloud environments. You will play a critical role in bridging machine learning development and production operations, ensuring AI systems are reliable, secure, scalable, and aligned with business needs. 

Working in a fast-paced and collaborative environment, you will contribute to the end-to-end delivery of AI products, driving engineering excellence, operational efficiency, and responsible AI practices across the organization. 

Role Responsibilities: 

• Design, build, and maintain scalable MLOps platforms, frameworks, and deployment pipelines that support the reliable delivery of machine learning and generative AI solutions.  
• Develop and integrate cloud-native services, automation workflows, and CI/CD practices to improve operational efficiency, system reliability, and deployment velocity.  
• Implement monitoring, observability, tracing, and performance management capabilities to proactively identify issues and optimize production systems.  
• Drive process optimization initiatives that improve model lifecycle management, operational resilience, governance, and overall team effectiveness.  
• Contribute to data security, compliance, and governance standards by ensuring appropriate controls, monitoring, and responsible management of AI and data assets.  
• Support knowledge sharing, technical storytelling, and documentation to increase transparency, adoption, and understanding of AI capabilities across the organization.  
• Mentor and support junior engineers, helping develop MLOps and AI engineering capabilities across the team.  
• Stay current with emerging technologies, AI engineering practices, and industry trends, identifying opportunities to enhance our platforms and delivery approaches.  

About You:

• A degree in Software Engineering, Computer Science, Artificial Intelligence, or a related technical field.  
• Strong experience developing software solutions using Python and modern engineering practices.  
• Experience with GitHub, Docker, and machine learning frameworks such as PyTorch or TensorFlow.  
• Hands-on experience working with cloud platforms such as Azure, AWS, or GCP.  
• Experience building APIs and services using FastAPI or similar frameworks. 
• Knowledge of CI/CD pipelines, automated testing frameworks, and GitHub Actions.  
• Experience implementing observability, monitoring, and tracing solutions for AI and machine learning applications, including tools such as Langfuse or equivalent platforms.  
• Understanding of machine learning lifecycle management, deployment strategies, and production monitoring.  
• Experience deploying and monitoring AI agents and LLM-based applications is considered an advantage. 

Having a good command of English is important; collaboration is important in our day to day work, so being able to communicate your ideas and understand others’ is key.