Who we are
At Ernst & Young we support you in achieving your unique potential both personally and professionally. We give you stretching and rewarding experiences that keep you motivated, working in an atmosphere of integrity and teaming with some of the world's most successful companies. And while we encourage you to take personal responsibility for your career, we support you in your professional development in every way we can. You enjoy the flexibility to devote time to what matters to you, in your business and personal lives. At Ernst & Young you can be who you are and express your point of view, energy and enthusiasm, wherever you are in the world. It's how you make a difference.
Requirements
- Bachelor's degree in Computer Science, Data Science, AI, Engineering, or related field.
- 3-5 years of experience in AI, Machine Learning, Data Science, or Analytics.
- Strong Python skills (Pandas, NumPy, Scikit-learn) and experience with PyTorch or TensorFlow.
- Good SQL proficiency and experience working with enterprise datasets.
- Experience deploying and integrating ML/AI models through APIs, cloud services, or workflows.
- Understanding of model evaluation, explainability, responsible AI, and production support.
- Familiarity with Git, Agile methodologies, documentation, and code review practices.
- Strong communication skills in English and Bahasa Indonesia.
- Preferred: Experience with Azure AI, Azure ML, Azure OpenAI, Copilot Studio, Azure DevOps/GitHub Actions, and Microsoft security/governance tools.
- Preferred certifications: AI-900, AZ-900, Azure AI Engineer Associate, Azure Data Scientist Associate.
Responsibilities
- Design, develop, test, and deploy AI/ML and Generative AI solutions for business processes.
- Build predictive, classification, recommendation, extraction, summarization, and anomaly detection models.
- Develop AI agents and Retrieval-Augmented Generation (RAG) solutions.
- Implement AI solutions using Azure AI Services, Azure OpenAI, Azure AI Search, and Azure ML.
- Perform data preparation, feature engineering, model training, validation, and monitoring.
- Develop data and model pipelines using Python and SQL.
- Monitor model performance, accuracy, drift, and operational issues after deployment.
- Maintain documentation on models, controls, and compliance requirements.
- Collaborate with business stakeholders to identify AI use cases and deliver prototypes/MVPs.
- Communicate AI outputs and recommendations in business-friendly language.
