Machine Learning Engineer | Financial Service

You will drive our banking supervision transformation by designing, developing, and deploying production-grade generative AI and machine learning solutions using large-scale financial datasets. Your impact will directly enhance risk identification, data analytics efficiency, and system scalability within a dynamic project team.

Responsibilities

• Participate in functional, technical, and design discussions with stakeholders to ensure best practices are followed throughout implementation.
• Collaborate with development teams to productionise AI solutions and transition them from development environments into secure and reliable production environments.
• Conduct feasibility studies and proofs of concept to evaluate and recommend optimal ML and GenAI approaches for specific banking supervisory use cases.
• Design, implement, and maintain end-to-end AI and GenAI solutions, including data ingestion, feature engineering, model training, validation, deployment, monitoring, and lifecycle management, while developing automation scripts for efficient and scalable operations.
• Productionise ML models and GenAI components by containerising services and implementing model-serving APIs, CI/CD pipelines, model versioning, automated testing, and scalable deployment patterns.
• Develop LLM-based solutions using modern framework and agentic workflows, with appropriate guardrails.

Requirement

• Hold a Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related discipline.
• At least 3 years of hands-on experience designing, building, and deploying AI-driven applications, with experience in financial services or regulated industries highly desirable.
• Demonstrate strong proficiency in Python and AI/ML libraries such as TensorFlow, PyTorch, scikit-learn, and pandas.
• Leverage familiarity with data warehouses, SQL, workflow orchestration tools such as Airflow, and business intelligence tools such as Tableau.
• Apply experience with production engineering practices, including API development, containerisation, version control, automated testing, CI/CD, and model lifecycle or serving platforms such as MLflow and Ray Serve.
• Possess experience with LLMs, including prompt engineering, retrieval-augmented generation (RAG), and agentic workflows, as well as working knowledge of MLOps and LLMOps practices.