Job Description

Singapore, Singapore, Singapore






Department
Data Science and Analytics
Job posted on
Jun 25, 2025
Employment type
Permanent - Full Time

Singlife is a leading homegrown financial services company, offering consumers a better way to financial freedom. Through innovative, technology-enabled solutions and a wide range of products and services, Singlife provides consumers control over their financial wellbeing at every stage of their lives.


In addition to a comprehensive suite of insurance plans, employee benefits, partnerships with financial adviser channels and bancassurance, Singlife offers investment and advisory solutions through its GROW with Singlife platform. It also offers the Singlife Account, a mobile-first insurance savings plan.


Singlife is the exclusive insurance provider for the Ministry of Defence, Ministry of Home Affairs and Public Officers Group Insurance Scheme. Singlife is also an official signatory of the United Nations Principles for Sustainable Insurance and the United Nations-supported Principles for Responsible Investment, affirming its commitment to finding a better way to sustainability.


The merger of Aviva Singapore and Singlife was announced in September 2020 and created one of the largest homegrown financial services companies in Singapore in a deal valued at S$3.2 billion. It was the largest insurance deal in Singapore at the time. Singlife was subsequently acquired by Sumitomo Life in March 2024, one of Japan's leading life insurers, which valued Singlife at S$4.6 billion, making the transaction one of the largest insurance deals in Southeast Asia.

Responsibilities:

Lead the design and implementation of end-to-end AI projects, from data collection to model development, deployment, and monitoring. Architect and maintain large-scale, AI-embedded distributed applications, ensuring high availability, low latency, and scalability for production environments. Write production-grade Python code to optimize the performance, reliability, and resilience of machine learning models and pipelines. Automate model training, testing, deployment, and monitoring for batch and real-time serving, supporting high-throughput, low-latency applications. Develop, manage and maintain AWS-based ML infrastructure. Proactively identify and resolve pipeline issues, ensuring the reliability and efficiency of ML systems in production. Mentor junior engineers and data scientists, fostering a culture of technical excellence and collaboration. Stay at the forefront of AI/ML advancements by engaging with the latest research, attending conferences, and introducing innovative solutions to the team.

Required Qualifications

5+ years of relevant experience, in machine learning, MLOps, or AI engineering. Bachelor's or Master's degree in Data Science, Computer Science, or a related field. Deep expertise in machine learning workflows, algorithms, and model deployment in production environments. Strong proficiency in Python, with solid understanding of software engineering best practices including object-oriented programming (OOP), clean architecture, design patterns, SOLID principles, and domain-driven design (DDD). Experience with containerization and orchestration tools (e.g., Docker, Kubernetes, or AWS ECS) for deploying and managing scalable, production-grade ML workloads. Excellent written and verbal communication skills to articulate complex technical concepts to technical and non-technical stakeholders.

Preferred Qualifications

Hands-on experience with maintaining large-scale, ML-embedded distributed applications in cloud environments Proficiency in Infrastructure as Code (IaC) tools, specifically Terraform and CloudFormation, for managing cloud infrastructure. Experience with GPUs and cloud-based training of deep neural networks. Experience with cloud technologies is ideal (AWS/GCP/Azure etc.) Experience with experimentation frameworks (e.g., weights & Biases, MLflow, Sagemaker experiments) for model tracking and monitoring. GenAI and LLMOps exposure (e.g., frameworks like LangChain, LlamaIndex, LangGraph, AutoGen; prompt management/tracing with Langfuse/LangSmith; vector databases like Weaviate, Qdrant, etc.; retrieval (Faiss, Opensearch,) or LLMOps solutions like vLLM) with experience in at least two areas. Experience with data engineering tools (Airflow, dbt) is a plus. Hands-on experience in training machine learning models, including data pre-processing, model tuning, and performance evaluation. * Highly motivated individual with strong curiosity to apply best practices in Machine Learning.