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Julius Baer

Machine Learning Engineer

Julius Baer is hiring a Machine Learning Engineer in Singapore, Singapore. Level rates it ; you can apply on Julius Baer.

Level's read on this job

  • On Level's scale, this machine learning engineer role is Builds AI ●●●●: the job is building AI systems.
  • Tools and skills the posting cites include PyTorch, TensorFlow and RAG.
  • Level lists 191 other open roles at Julius Baer.

AI in this role

pytorchtensorflow
ragai-agents

At Julius Baer, we celebrate and value the individual qualities you bring, enabling you to be impactful, to be entrepreneurial, to be empowered, and to create value beyond wealth. Let’s shape the future of wealth management together.

GENERAL 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.

YOUR CHALLENGE

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.

YOUR PROFILE

SKILLS REQUIREMENTS

Personal and Social

  • 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

Professional and Technical

  • 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

We are looking forward to receiving your full job application through our online application tool. Further interesting job opportunities can be found on our Career site.

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How we rate this

Machine Learning Engineer at Julius Baer rates 98 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGAI agentsPyTorchTensorFlow

Also common in machine learning engineer jobs at this level

Pay

Typical machine learning engineer job
$200k

This posting doesn't list pay. Use the typical machine learning engineer figure when pay comes up. See machine learning engineer jobs

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. How do you decide when an AI agent can act on its own versus asking for approval first?
  3. What are the limits of PyTorch that you've run into, and how did you work around them?
  4. What's a project where you used TensorFlow hands-on?
  5. How would you decide a model or AI system is ready to ship?

Adapt your resume

  • List these exact terms on your resume: RAG, AI agents, PyTorch, and TensorFlow. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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