Dai-ichi Life Vietnam

I. Experience Requirements

1. AI, Machine Learning & Deep Learning

• Expertise in Supervised/Unsupervised Learning, Computer Vision, and Natural Language Processing (NLP).
• Deep understanding of Deep Learning architectures and Statistics/Mathematics.
• Proficiency in frameworks: PyTorch, TensorFlow, and Scikit-Learn.

2. Generative AI & Large Language Models (LLMs)

• Experience with LLMs, AI Agents, and Prompt Engineering.
• Hands-on implementation of RAG (Retrieval-Augmented Generation) and model Fine-tuning.
• Development using tools like LangChain and OpenAI APIs.

3. Data & Software Engineering

• Python mastery (Pandas, NumPy) and SQL for complex data manipulation.
• Building scalable Data Pipelines and Data Engineering workflows (Apache Spark).
• Adherence to SOLID Principles, Git version control, and clear Documentation.

4. Cloud, DevOps & MLOps

• Multi-cloud proficiency: K8s, Openshift, AWS, Azure, GCP.
• Containerization and Orchestration: Docker and Kubernetes.
• Implementing CI/CD pipelines and MLOps for automated model deployment and monitoring.

II. Personality Requirements

• Innovation & Problem Solving: A proactive mindset to tackle "Use Case" challenges and drive technological advancement.
• Collaboration & Communication: Ability to explain complex AI metrics to non-technical stakeholders and work within Agile Methodologies.
• Organization & Flexibility: Managing multiple tasks in a fast-paced environment while maintaining Compliance Management standards.
• Leadership: Ability to influence or support project direction and inspire technical excellence across the organization

III. Additional Skills and Attributes

• Continuous Learning: Commitment to continuous learning and staying updated with the latest industry trends and technologies.
• Attention to Detail: Keen attention to detail to ensure high-quality code and user satisfaction.
• Customer Focus: Strong focus on understanding and meeting user needs.
• Project Management: Basic project management skills to handle small to medium-sized projects efficiently.
• Agile Development Methodology: understanding of the Scrum framework, including roles, events, and artifacts

IV. Qualification

• Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or a related quantitative field.
• Proven experience in end-to-end Machine Learning lifecycles.
• Relevant Cloud certifications (AWS/Azure/GCP) are a plus.