Lead Data and AI Solution Engineer

Great Eastern; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

Great Eastern seeks an experienced AI/GenAI Engineer to design and deploy scalable AI solutions across data pipelines and applications in a Singapore setting. You will collaborate with stakeholders to translate business needs into technical specs, build GenAI models, and deliver production-ready systems.

The role spans data engineering, cloud ML platforms, and full-stack development, focusing on robust, scalable GenAI implementations for diverse business use cases.

Qualifications

• Requirement Analysis & Solution Design.
• Collaborate with stakeholders to understand business needs, translate requirements into technical specifications, and architect GenAI solutions.
• Design and maintain efficient data pipelines for preprocessing, cleaning, and augmenting datasets.
• End-to-End GenAI Application Development including frontend and backend services.
• DevOps/MLOps: CI/CD, containerization (Docker, Kubernetes), and model monitoring tools (MLflow).
• Cloud Platforms: AWS, Azure, GCP with SageMaker/Vertex AI/Azure ML; IaC via Terraform/CloudFormation.
• Data Engineering: Experience with scalable data pipelines (ETL/ELT) using Spark, Airflow, Databricks.

Responsibilities

• Implement and deliver advanced data and AI solutions that drive innovation and enhance operational efficiency.
• Develop and deploy generative AI models (e.g., LLMs, GANs) on AWS.
• Deploy scalable GenAI solutions into production environments with robust reliability.
• Cross-functional collaboration with data scientists, software engineers, and business teams.
• Seamlessly integrate GenAI solutions into client infrastructure via APIs and microservices.
• Design, develop, and deploy user-facing applications powered by GenAI (frontend and backend).

Job description

• Implement and deliver advanced data and AI solutions that drive innovation, optimize decision-making, and enhance operational efficiency—contributing directly to the organization's strategic growth and technological leadership.
  
• Requirement Analysis & Solution Design
  
• Collaborate with stakeholders to understand business needs, translate requirements into technical specifications, and architect tailored GenAI solutions (e.g., chatbots, content generators) that align with project goals.
  
• Development & Deployment of GenAI Models Develop and deploy generative AI models (e.g., LLMs, GANs) on AWS.
  
• Deploy scalable solutions into production environments, ensuring robustness and reliability.
  
• Cross-functional Collaboration Partner with data scientists, software engineers, and business teams to integrate AI capabilities into workflows, ensuring alignment with technical and operational objectives.
  
• Design and maintain efficient data pipelines for preprocessing, cleaning, and augmenting datasets.
  
• Ensure data quality, governance, and compliance with privacy regulations System Integration & API Development Seamlessly integrate GenAI solutions into existing client infrastructure (e.g., cloud platforms, enterprise systems). Develop APIs and microservices to enable real-time AI functionality.
  
• End-to-End GenAI Application Development Design, develop, and deploy user-facing applications powered by GenAI, including frontend interfaces (e.g., React) and backend services. Integrate AI models into applications to deliver seamless
• AI/ML Engineering: 3–5 years of hands-on experience in designing, developing, and deploying machine learning/AI solutions, with 1–2 years focused on generative AI (e.g., LLMs, GANs, diffusion models).
  
• Application Development: Proven track record in full-stack or backend development, including building and deploying AI-powered applications (e.g., chatbots, content automation tools).
  
• Cloud Platforms: Experience with cloud services (AWS, Azure, GCP) for AI/ML workflows (e.g., SageMaker, Vertex AI, Azure ML) and infrastructure-as-code (Terraform, CloudFormation). Data Engineering: Expertise in building scalable data pipelines (ETL/ELT) using tools like Apache Spark, Airflow, or Databricks.
  
• DevOps/MLOps: Familiarity with CI/CD pipelines, containerization (Docker, Kubernetes), and model monitoring tools (MLflow).