Machine Learning Engineer

BANK JULIUS BAER & CO. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

BANK JULIUS BAER & CO. LTD. is seeking a skilled Machine Learning Engineer to design, build, and maintain scalable AI solutions within a DevOps environment.

You will work across the full lifecycle from concept to deployment, collaborating with cross-functional teams to deliver high-quality digital solutions. Key responsibilities include developing ML/AI solutions, creating multi-agent systems, grounding AI with enterprise data, and ensuring reliable performance through evaluation and testing.

Qualifications

• Bachelor's or Master's degree in Data Science, Computer Science, Mathematics or Statistics.
• Proven experience with Python and machine learning libraries.
• Experience with Large Language Models (LLMs) and AI agent systems.
• Understanding of AI agents, orchestration frameworks and reasoning patterns.
• Experience with data preprocessing, feature engineering and model evaluation.

Responsibilities

• Develop, deploy, and optimize ML/AI solutions addressing business challenges.
• Build multi-agent systems with function/tool-calling capabilities.
• Design and implement RAG systems grounded in enterprise data.
• Evaluate and integrate AI models for performance and reliability.
• Collaborate with engineers, data scientists and stakeholders to integrate ML solutions.
• Contribute to code reviews, testing, and debugging to ensure quality.

Skills

Python

ML libraries

LLMs

AI agents

Agent orchestration

Education

Bachelor's or Master's in data science / CS / maths

Tools

TensorFlow

PyTorch

Job description

We are seeking a skilled machine learning engineer (ML Eng) to join one of our agile development teams in our ML & AI ART. In this role, you will play a key part in designing, building, and maintaining robust, scalable AI solutions within a DevOps environment. You'll contribute across the entire lifecycle - from concept to deployment - and collaborate closely with cross-functional teams to deliver high-quality digital solutions.

KEY FEATURES AND ACCOUNTABILITIES

Key Responsibilities

• Develop, deploy, and optimize machine learning and AI solutions that address complex business challenges.
• Create multi-agent systems and equip AI models with function/tool-calling capabilities.
• Design and implement RAG systems to ground AI responses in enterprise data.
• Assess and integrate AI models (e.g. LLMs) ensuring optimal performance and reliability.
• Optimize agentic workflows and AI agents for production use cases.
• Testing and optimizing system prompts and few-shot example to ensure accurate, consistent, and safe AI outputs.
• Performance evaluation of machine learning and AI models using appropriate metrics, evaluation sets and techniques, and continuously iterate and improve upon them.
• Collaborate with platform teams, data engineers, data scientists, and other stakeholders to integrate machine learning solutions into existing systems and processes.
• Participate in code reviews, testing, and debugging to ensure the quality and reliability of machine learning solutions.

Regulatory Responsibilities &/OR Risk Management

• Demonstration of appropriate values and behaviours including but not limited to standards on honesty and integrity, due care and diligence, fair dealing (treating customers fairly), management of conflicts of interest, competence and continuous development, adequate risk management, and compliance with applicable laws and regulations.

PROFESSIONAL AND TECHNICAL SKILLS REQUIREMENTS

• Bachelor's or Master's degree in Data Science, Computer Science, Mathematics, Statistics, or a related field.
• Strong programming skills in Python with experience in machine learning libraries and deep learning frameworks such as TensorFlow or PyTorch.
• Proven experience working with Large Language Models (LLMs).
• Good understanding of AI agents & agentic workflows, LLM orchestration frameworks and reasoning patterns.
• Experience with data pre-processing, feature engineering, and model selection and evaluation techniques.
• Knowledge of statistical and mathematical concepts relevant to machine learning, such as probability, linear algebra, and optimization.
• Understanding software development best practices, including version control, testing, and documentation.
• Excellent problem-solving and debugging skills, with the ability to identify and resolve issues quickly and effectively.
• Relevant work experience in machine learning, data science or a related field.Strong problem-solving and analytical skills, with the ability to think critically and creatively about complex challenges.
• Excellent communication and collaboration skills, with the ability to work effectively with cross-functional teams and stakeholders at all levels of the organization.
• Ability to manage personal workloads effectively, to prioritize tasks, manage timelines, and deliver high-quality results on schedule.
• Continuous learning mindset, with a passion for staying up to date with the latest advancements in machine learning and artificial intelligence.
• Attention to detail and commitment to producing high-quality, reliable, and maintainable code.