Your Responsibilities:
Design and deliver scalable AI and machine learning solutions across underwriting, risk, and operations
Own the end-to-end ML lifecycle, from feature engineering to deployment and monitoring
Build and maintain data pipelines and production workflows using Python, TensorFlow, PyTorch, scikit-learn, AWS (S3, Lambda, SageMaker, Step Functions, Bedrock), Snowflake, and Dataiku
Apply MLOps best practices, including CI/CD, automated testing, model versioning, and observability
Define deployment standards and track model performance in production
Contribute to GenAI/LLM initiatives and reusable solution designs
Ensure compliance with governance, risk, and responsible AI standards
Collaborate with cross-functional teams to translate business needs into practical AI solutions
Your Experience:
3+ years experience in machine learning, data science, and software development
Proficient in Python and/or R
Experience with cloud platforms (e.g. AWS), Linux, and containerised environments
Familiar with modern AI/GenAI approaches (e.g. RAG)
Good understanding of data modelling, governance, and security
Experience with Agile, DevSecOps/DataOps, and testing in production environments
Strong problem-solving skills with a proactive, collaborative mindset
If interested in learning more, please apply directly or email me - (url removed)