Senior ML Ops - Content Creation Technology Group

The Content Creation Technology Group (CCTG) is looking for a Senior ML Ops to work in a team that develops machine learning tools to support teams in creating, testing, and deploying ML solutions to games. You will support and collaborate with Data Scientists, Software Developers and other stakeholders in delivering these ML solutions. The Senior ML Ops role will bring best practices and design principles to assist the team in delivering our ambitious features roadmap. 

Main responsibilities: 

• Designing and developing machine learning systems and schemes based on the business requirements and objectives. 

• Optimizing existing tools for improved performance, scalability, and efficiency. 

• Contribute to the industrialization of machine learning projects, focusing on building pipelines and workflows.  

• Adapting and improving models’ usage, facilitating tools usage, and adapting these to users needs. 

• Create tools to support and integrate the ML models inside a multi-process pipeline. 

• Enhancing the existing machine learning libraries and frameworks. 

• Collaborating with game production teams, data science team, research team and other stakeholders. 

Other responsibilities: 

• Conducting research to stay up to date with the latest advancements. 

• Documentation, presentations, and knowledge sharing to communicate complex AI concepts to both technical and non-technical collaborators. 

• Creating proof of concepts to satisfy investigations into product directions. 

Education: 

• Bachelor’s degree in computer science or computer engineering or equivalent. A specialization in Machine Learning is an asset. 

Relevant Experience: 

• A minimum of 5 years of experience in software engineering. 
• A minimum of 2 years of experience in machine learning. 

Skills: 

• A good knowledge of a production machine learning pipeline; from ML training and deployment to model performance-tracking/behavioral-drift. 

• Proficient in Python and have experience with libraries and frameworks such as PyTorch and/or TensorFlow. 

• A familiarity with MLOps platforms such as Databricks and/or ClearML. 

• A good knowledge of container technology such as Docker and/or Kubernetes. 

• A good knowledge of machine learning and deep learning fundamentals. 

• A good knowledge of Database products is an asset. 

• A good knowledge of cloud-based platforms is an asset.