Senior AI Engineer

WHO WE ARE

As Singapore’s longest established bank, we have been dedicated to enabling individuals and businesses to achieve their aspirations since 1932. How? By taking the time to truly understand people. From there, we provide support, services, solutions, and career paths that meet their individual needs and desires.

Today, we’re on a journey of transformation. Leveraging technology and creativity to become a future-ready learning organisation. But for all that change, our strategic ambition is consistently clear and bold, which is to be Asia’s leading financial services partner for a sustainable future.

We invite you to build the bank of the future. Innovate the way we deliver financial services. Work in friendly, supportive teams. Build lasting value in your community. Help people grow their assets, business, and investments. Take your learning as far as you can. Or simply enjoy a vibrant, future-ready career.

Your Opportunity Starts Here.

About the Role

We are seeking an experienced Senior AI Engineer to join our team and drive the development of scalable, production-grade AI solutions. The ideal candidate will combine deep expertise in artificial intelligence and machine learning with strong software engineering fundamentals, particularly in microservices architecture and multi-language development.

Responsibilities

Required Qualifications

What we offer

Competitive base salary. A suite of holistic, flexible benefits to suit every lifestyle. Community initiatives. Industry-leading learning and professional development opportunities. Your wellbeing, growth and aspirations are every bit as cared for as the needs of our customers.

Responsibilities

Design, develop, and deploy machine learning models and AI solutions that solve complex business problems.; Build and implement agentic AI systems with autonomous decision-making, reasoning, and task execution capabilities; Develop multi-agent systems that use tools, planning, and memory.; Build and optimise large-scale ML pipelines for training, evaluation, and inference.; Implement state-of-the-art algorithms in areas such as natural language processing, computer vision, recommendation systems, or predictive analytics; Conduct experiments, perform model evaluation, and iterate on ML solutions to improve performance.; Stay current with the latest AI/ML research and identify opportunities to apply emerging techniques.; Design and implement robust microservices architectures for AI/ML applications; Build scalable, maintainable, and well-documented code across multiple programming languages.; Develop RESTful APIs and event-driven systems to integrate AI capabilities into broader platforms; Ensure high availability, fault tolerance, and performance optimisation of AI services.; Implement CI/CD pipelines for automated testing and deployment of ML models.; Mentor junior engineers and data scientists on best practices in AI engineering and software development; Lead technical design discussions and contribute to architectural decisions.; Collaborate with cross-functional teams, including product managers, data scientists, and platform engineers.; Conduct code reviews and promote engineering excellence across the team; Drive technical innovation and champion the adoption of new tools and methodologies

Requirements

AI/ML Expertise – 5+ years of experience developing and deploying machine learning models in production environments; Agentic AI – Experience building autonomous AI agents with capabilities including tool use, reasoning, planning, and multi-step task execution; Programming Languages – Advanced proficiency in at least Python and Java.; Experience with Deep learning frameworks (TensorFlow, PyTorch), data processing (pandas, NumPy), ML libraries (scikit-learn), etc.; Experience with Spring Boot, microservices frameworks, and concurrent programming.; Microservices Architecture – 5+ years of hands-on experience designing and implementing microservices-based systems; ML Operations – Experience with MLOps practices, model versioning, monitoring, and deployment strategies; Cloud Platforms – Proficiency with AWS, GCP, or Azure ML services and infrastructure; Strong understanding of distributed systems, message queues (Kafka, etc.), and event-driven architectures; Experience with containerization (Docker) and orchestration (Kubernetes); Knowledge of database systems (SQL and NoSQL) and data engineering concepts; Familiarity with API design principles and RESTful services; Experience with version control (Git) and collaborative development workflows; Bachelor's or master’s degree in Computer Science, Engineering, Mathematics, or a related field (or equivalent experience).; Proven track record of delivering complex AI projects from conception to production; Strong problem-solving abilities and analytical thinking; Excellent communication skills with the ability to explain complex technical concepts to diverse audiences; Experience working in agile development environments

Nice to have

PhD in Machine Learning, Artificial Intelligence, or related field; Experience with agentic AI frameworks (LangChain, AutoGen, etc.) and agent orchestration patterns; Hands-on experience implementing Agents patterns such as ReAct, Plain and Execute, etc.; Hands-on experience with advanced prompting techniques such as Chain-Of-Thought, etc.; Experience with transformer architectures and large language models (LLMs); Knowledge of vector databases and retrieval-augmented generation (RAG) systems; Contributions to open-source AI/ML projects; Publications in top-tier AI/ML conferences or journals; Experience with real-time inference systems and model optimisation techniques; Knowledge of federated learning, edge AI, or distributed training; Familiarity with MLflow, Kubeflow, or similar ML platforms; Experience with multiple cloud providers and multi-cloud architectures; Experience with building LoRA/QLoRA, PEFT pipelines for enterprise-specific tuning; Experience implementing distributed training pipelines (Ray, FSDP, DeepSpeed, PyTorch Distributed, etc.); Experience with integrating continuous feedback loops to improve the model incrementally