Not specified; not specified; flexible working opt...
Aws sagemaker operational experience
Python programming and automation skills
Ci/cd pipeline development for ml
The role focuses on building a world-class, self-service, secure, and scalable MLOps platform to enable rapid experimentation and safe production deployment of ML and Generative AI models
Job Summary
The role focuses on building a world-class, self-service, secure, and scalable MLOps platform to enable rapid experimentation and safe production deployment of ML and Generative AI models.
You will operationalize machine learning and Large Language Models at scale using AWS SageMaker while ensuring compliance and observability for critical banking remediation use cases.
This position offers the opportunity to drive continuous improvement in platform reliability, security, cost, and performance within a large-scale enterprise environment.
Matching Summary
The role focuses on building a world-class, self-service, secure, and scalable MLOps platform to enable rapid experimentation and safe production deployment of ML and Generative AI models.
Salary
Not specified; Not specified; Flexible working options available
Skills & Requirements
Must-have
AWS SageMaker operational experience
Python programming and automation skills
CI/CD pipeline development for ML
LLM and Generative AI workload support
Infrastructure as Code (Terraform or CloudFormation)
Docker containerized ML workloads
Nice-to-have
Experience in regulated enterprise environments
Strong problem-solving with engineering approach
Mentoring junior team members
Data drift and model performance monitoring
Collaboration with data scientists
Key Requirements
Hands-on experience operationalising ML models in cloud