台北市松山區8年以上大學以上
【 Position Overview】
Lead and manage AI/ML operations to drive innovation and implementation of artificial intelligence solutions for our FinTech products. Ensure scalable, reliable, and compliant AI systems across cloud and on-premise environments while managing team operations and strategic initiatives.
【Key Responsibilities】
1. Team Leadership: Lead and manage AI/ML team operations, budget, and strategic initiatives
2. AI/ML Development: Plan, develop, deploy, and maintain AI/ML models and pipelines in production environments
3. LLM Operations: Manage Large Language Model deployment, fine-tuning, integration, and governance frameworks
4. Infrastructure Management: Oversee AI infrastructure operations including model serving, monitoring, and MLOps workflows
5. Cross-functional Collaboration: Coordinate with Product, Engineering, Risk, and Compliance teams to ensure AI solutions meet business and regulatory requirements
6. Compliance & Governance: Support internal and external compliance audits related to AI governance and model risk management
7. Performance Optimization: Maintain AI system performance, reliability, security, and cost optimization
8. Strategic Planning: Drive AI strategy and roadmap aligned with business objectives
9. Responsible AI: Ensure responsible AI practices, ethical implementation, and LLM safety measures
【Core Skills】
1. Excellent communication and problem-solving skills
2. Experience with AI model validation and testing methodologies
3. Knowledge of LLM evaluation metrics and benchmarking
【Education & Experience】
1. Bachelor's degree in Computer Science, Data Science, Machine Learning, or related field
2. Minimum 8 years of AI/ML experience with at least 3 years in team management
3. Experience supporting AI governance and model risk management audits
4. Understanding of financial regulations related to AI/ML usage
【Technical Expertise】
1. LLM Technologies: GPT, BERT, LLaMA, Claude, transformer architectures, fine-tuning, prompt engineering, RAG
2. ML/AI Frameworks: TensorFlow, PyTorch, Hugging Face Transformers
3. MLOps & Infrastructure: MLflow, Kubeflow, Docker, Kubernetes, model serving, GPU optimization
4. Cloud Services: AWS SageMaker, Azure OpenAI, Google Vertex AI
5. Programming: Python, R, SQL, JavaScript
6. Data Technologies: Spark, Hadoop, Kafka, vector databases, ETL processes
7. DevOps: Git, CI/CD for ML workflows, model monitoring, A/B testing