全职 ML-AI Engineer 工作, 薪水, Tech Aalto Pte Ltd 公司招聘中 - Ricebowl

ML/AI Engineer

• Education: Degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Systems or a related technical discipline.
• Experience: 3–5 years of hands-on experience building and deploying ML/AI applications. This must include at least one LLM-enabled system delivered beyond prototype into operational use.

Technical & Professional Competencies

• Strong Python software engineering capability, including API development, testing, version control and modular system design.
• Hands-on experience building and deploying ML/AI applications, including at least one LLM-enabled or agentic AI solution beyond proof-of-concept stage.
• Experience with LLM applications, retrieval, tool use, workflow orchestration or multi-agent frameworks such as LangGraph, CrewAI or equivalent.
• Experience integrating AI/ML solutions with enterprise data platforms, databases, APIs or cloud services.
• Ability to design and implement systematic evaluation frameworks for AI systems, including testing, benchmarking, observability and reproducibility.
• Working knowledge of AI governance, security and Responsible AI practices, including human-in-the-loop controls, access management, auditability and safeguards against misuse.
• Strong problem-solving and analytical skills, with the ability to translate business requirements into scalable AI-enabled solutions. Preferred (Advantageous)
• Experience with AWS services such as Bedrock, Redshift, Glue, S3, Lambda or related cloud-native services.
• Experience with PySpark, Databricks or large-scale data processing and feature engineering pipelines.
• Experience with CI/CD, containerisation, MLOps or model deployment tooling (e.g. Docker, MLflow, GitHub Actions, Jenkins).
• Experience implementing monitoring, observability and operational support processes for ML/AI applications.
• Experience delivering fraud detection, risk, compliance, investigation or decision support solutions.

Experience with knowledge retrieval, RAG, graph-based workflows, prompt management, model evaluation tooling or AI observability platforms.

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