Data Engineer
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Summary
Data Engineer in Singapore (5+ years) designing agentic AI workflows with LangGraph and LLMs, including Agent-to-Agent orchestration and prompt engineering. Day to day involves production-grade Python with pytest, GitLab CI/CD, reusable data components, and MLOps pipelines (MLflow, Kubeflow, Azure ML) on Azure/Kubernetes.
Job Role : Data Engineer
Job Location : Singapore
Experience : 5+ Years
Roles & Responsibilities
Design and develop reusable agentic AI workflows using LangGraph and LLMs Implement Agent-to-Agent (A2A) orchestration patterns Perform prompt engineering to ensure reliable and controlled LLM outputs Write clean, maintainable, and production-grade Python code with pytest unit tests Build reusable data transformation components Use Git for version control and GitLab CI/CD for automated testing and deployment Collaborate with stakeholders to convert business needs into technical specifications Support MLOps pipelines for model training, tracking, and deployment Adapt to evolving project requirements and take on additional responsibilities as needed Required Skills & Qualifications
Proficiency in Python, including unit testing with pytest Strong problem-solving and logical reasoning skills Experience with agentic AI system design and orchestration Hands-on experience with LangGraph workflows and reusable components Solid understanding of A2A orchestration principles Good prompt engineering skills for LLM control and output consistency Experience with Git and GitLab CI/CD in a team environment Knowledge of Python, SQL, and ML libraries (scikit-learn, PyTorch, TensorFlow) Experience with MLOps tools (MLflow, Kubeflow, Azure ML) Familiarity with Azure cloud and/or Kubernetes environments Ability to work effectively in a fast-changing environment Good to Have
Experience with Java (Spring Boot) and/or React Exposure to financial domain data (risk, trading, KYC, etc.) Advanced experience with LangGraph and A2A architectures
Job Location : Singapore
Experience : 5+ Years
Roles & Responsibilities
Design and develop reusable agentic AI workflows using LangGraph and LLMs Implement Agent-to-Agent (A2A) orchestration patterns Perform prompt engineering to ensure reliable and controlled LLM outputs Write clean, maintainable, and production-grade Python code with pytest unit tests Build reusable data transformation components Use Git for version control and GitLab CI/CD for automated testing and deployment Collaborate with stakeholders to convert business needs into technical specifications Support MLOps pipelines for model training, tracking, and deployment Adapt to evolving project requirements and take on additional responsibilities as needed Required Skills & Qualifications
Proficiency in Python, including unit testing with pytest Strong problem-solving and logical reasoning skills Experience with agentic AI system design and orchestration Hands-on experience with LangGraph workflows and reusable components Solid understanding of A2A orchestration principles Good prompt engineering skills for LLM control and output consistency Experience with Git and GitLab CI/CD in a team environment Knowledge of Python, SQL, and ML libraries (scikit-learn, PyTorch, TensorFlow) Experience with MLOps tools (MLflow, Kubeflow, Azure ML) Familiarity with Azure cloud and/or Kubernetes environments Ability to work effectively in a fast-changing environment Good to Have
Experience with Java (Spring Boot) and/or React Exposure to financial domain data (risk, trading, KYC, etc.) Advanced experience with LangGraph and A2A architectures