Machine Learning Engineer

Job Summary

As a Machine Learning Engineer, you will design, build, and operate production-grade ML and

AI systems that power intelligent experiences across our digital banking platform. You will work closely with Data Scientists, Data Engineers, Product Owners, and Software Engineers to transform machine learning and AI models into scalable, reliable, and secure production services. You will own the end-to-end machine learning and AI lifecycle, from data and model deployment, monitoring, optimization, to continuous improvement, while delivering measurable business value and exceptional customer experiences in our AI-driven digital banking platform.

Job Responsibilities

• ML Systems Engineering: Design, develop, maintain, and optimize machine learning systems, including batch, real-time, and streaming inference patterns.
• AI Engineering: Build agentic applications and experiences, including conversational agents, workflow and operations automation, agentic RAG, personalized action agents.
• MLOps: Design and implement end-to-end modular MLOps pipelines, including experiment tracking, model versioning, CI/CD, feature management, and automated deployment workflows.
• ML/AI Infrastructure: Design, provision, and optimize secure, scalable cloud-native AI infrastructure using Infrastructure as Code (IaC) and platform engineering best practices to support production ML/AI systems.
• Observability & Reliability: Establish robust monitoring and drift detection systems to track functional and operational performances in real-time, actively identifying areas for optimisation to enhance scalability, efficiency, and reliability.

Job Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, or a related quantitative discipline.
• More than 2 - 3 years of experience in a machine learning role, or relevant experience in data science or engineering.
• Strong understanding of machine learning algorithms, feature engineering, and model evaluation. Experience working throughout the end-to-end machine learning lifecycle, including model deployment, monitoring, to continuous improvement.
• Familiarity with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) or AI agent frameworks (i.e. Langchain, Langgraph, Pydantic AI) is an advantage.
• Proficiency in Python and scripting languages like bash. Experience in engineering best practices (version control, testing, clean code principles, etc.)
• Experience building machine learning pipelines (e.g. Airflow, Metaflow), experiment tracking (MLflow), model versioning, CI/CD (e.g. Jenkins, Github Actions), and feature engineering workflows.
• Experience with containerization and orchestration technologies like Docker, Kubernetes, or equivalent.
• Familiarity with cloud-native applications on AWS, Azure, or GCP. Infrastructure as Code (Terraform, CloudFormation, etc.) experience is an added advantage.
• Strong analytical thinking, problem solving, and troubleshooting skills.
• Excellent communication and interpersonal skills, capable of effectively collaborating with diverse stakeholders from cross-functional agile teams.
• Agility and a proactive attitude towards embracing new technologies, contributing to the continuous innovation of our ML & AI capabilities.