Dai-ichi Life Vietnam

Tóm tắt công việc

Design, build, and deploy next-generation AI ML Solutions. The ideal candidate is a technical problem-solver who can bridge the gap between complex mathematical research, scalable engineering. You will be responsible for the full lifecycle of AI ML products, from data engineering and model training (RAG, Fine-tuning) to production deployment via MLOps and Kubernetes.

The AI ML Engineer is expected to work closely with cross-functional teams — including Business Analysts, QA Engineers, System Architects, and Product Manager, Business User — to deliver reliable AI systems aligned with business objectives. 

Beyond coding, this position involves technical leadership, mentoring, and continuous improvement in software engineering practices for the team

PRINCIPAL ACCOUNTABILITIES

• Analyze the business specs then prepare technical specs and develop the new AI ML Solutions or enhance existing Solutions for supporting business needs.
• Support the team leader on technical analysis to provide the best technical solution as well as support colleagues for solving technical issues to complete the assigned projects / tasks on timely manner.
• Investigate and answer daily enquiries on systems functional as well as problem encountered in order to resolve it in timely manner.
• In charge of unit test on enhancements / new development under responsibility to ensure developed modules are matched with business requirements.
• Assist users/team leader on building test scripts to cover all test cases.

• Salary for 13th month, Bonus & Special Bonus
• Full of social welfare under Vietnamese Labor Law (Insurance, annual leave, ...)
• Healthcare for yourself & Your Family
• Annual travel and team building activities
• 15-16 annual leave days
• Company will buy Life Insurance Contract for yourself after 1 year working
• Training: Trained in soft and technical skills
• International, challenging, and friendly working environment

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.