Your key responsibilities

• Model Deployment:
  Collaborate with data scientists to deploy machine learning models into production environments.
  Implement deployment strategies such as A/B testing or canary releases to ensure safe and controlled rollouts.
• Infrastructure Management:
  Design and manage the infrastructure required for hosting ML models, including cloud resources and on-premises servers.
  Utilize containerization technologies like Docker to package models and dependencies.
  Continuous Integration/Continuous Deployment (CI/CD):
  Develop and maintain CI/CD pipelines for automating the testing, integration, and deployment of ML models.
  Implement version control to track changes in both code and model artifacts.
  Monitoring and Logging:
  Establish monitoring solutions to track the performance and health of deployed models.
  Set up logging mechanisms to capture relevant information for debugging and auditing purposes.
• Scalability and Resource Optimization:
  Optimize ML infrastructure for scalability and cost-effectiveness.
  Implement auto-scaling mechanisms to handle varying workloads efficiently.
• Security and Compliance:
  Enforce security best practices to safeguard both the models and the data they process.
  Ensure compliance with industry regulations and data protection standards.
• Data Management:
  Oversee the management of data pipelines and data storage systems required for model training and inference.
  Implement data versioning and lineage tracking to maintain data integrity.
• Collaboration with Cross-Functional Teams:
  Work closely with data scientists, software engineers, and other stakeholders to understand model requirements and system constraints.
  Collaborate with DevOps teams to align MLOps practices with broader organizational goals.
• Performance Optimization:
  Continuously optimize and fine-tune ML models for better performance.
  Identify and address bottlenecks in the system to enhance overall efficiency.
• Documentation:
  Maintain clear and comprehensive documentation of MLOps processes, infrastructure, and model deployment procedures.
  Document best practices and troubleshooting guides for the team.

Your skills and experience

• University degree in a technical or quantitative field (e.g., computer science, mathematics, physics, economics, etc.), preferably a Master’s or Doctoral degree
• 2-4 years of experience in applying AI, machine learning and/or data science in business and/or academia. Strong knowledge of at least one programming language (e.g., Python, JavaScript) and relevant data science or engineering framework (e.g., scikit-learn, TensorFlow, Spark, etc.).
• Ideally, practical experience in finance and banking
• Comfortable working with and managing uncertainty and ambiguity
• Excellent oral and written communication skills in English