Machine Learning Engineer, National Jobs-Skills Data Office (Intd)

SSG is a dynamic and forward-thinking organization dedicated to empowering individuals and shapingthe future of Singapore's workforce. As the National Skills Authority, SSG leads the charge in driving the SkillsFuture movement, a national initiative that promotes lifelong learning and skills development. With a strong focus on innovation and collaboration, SSG works closely with employers, training providers, and individuals to create a vibrant ecosystem of learning and growth. By offering a wide range of initiatives, programs, and funding schemes, SSG enables individuals to unlock their full potential, acquire new competencies, and stay ahead in a rapidly changing job market. The National Jobs-Skills Data Office, under the Skills Intelligence and Planning Division, serves three core functions: · Dataand Algorithms Innovation and R&D: Undertake development of new data models and algorithms to serve whole of government’s jobs-skills intelligence needs; · Jobs-Skills Product Management, Development and Delivery: Manage and enhance jobs-skills products, which includes UX/UI design and end-to-end product life cycle management; · Data Management and Operations: Centrally manage data quality, data models and data infrastructure to support internal and external users Come join this game-changing team in SSG, where we empower employers, citizens, training providers, and policymakers to make informed decisions by using a user-centered approach,providing trusted source of jobs-skills data and insights, and a common set of jobs-skills taxonomies for a skills-first future! As a Machine Learning Engineer,you will be a key member of the Data Management and Operations team, designing,building and deploying scalable machine learning systems that power real-world applications. You will be creating robust data pipelines and integrating models into production environments. Working closely with data scientists, data engineers and software engineers, you will ensure that machine learning solutions are reliable, efficient and continuously monitored to maintain performance over time. • Design and implementscalable AI/ML infrastructure by aligning data warehouses, APIs, and downstream systems under a governed, scalable model that ensures seamless integration with existing systems while maintaining high performance and reliability standards • Develop and deployrobust AI solutions including the design, development, and deploymentof AI models that integrate seamlessly with existing systems, while evaluating and integrating third-party AI tools and frameworks to enhance analytical capabilities • Optimize and maintainML model performance through continuous fine-tuning of existing AImodels for performance, accuracy, and scalability, implementing automated monitoring systems for model performance including drift detection, latency monitoring, and resource utilization tracking • Build and maintain end-to-end data architecture by designing robust data systems that integrate ingestion, metadata, storage, and consumption layers across production environments, supporting large-scale datasets including Skills Framework data, job postings, and administrative data • Implement MLOps andCI/CD processes by establishing continuous integration and deploymentpipelines for ML models using version control, containerization, orchestration,and comprehensive testing environments to ensure reliable model deployment andupdates • Ensure systemreliability and governance compliance through implementation andoptimization of data pipelines in accordance with NJSDO data governance standards, diagnosing and resolving pipeline issues, and contributing to incident response processes and post-mortem reviews • Drive cross-functionalcollaboration by working closely with data engineers, product teams,and governance stakeholders to align model deployment with business requirements, translating technical capabilities into meaningful business outcomes that support workforce planning initiatives Requirements Experience: · Proficiency in data engineering practices with demonstrated experience building scalable data pipelines and managing large-scale data processing workflows · Strong programming skills in Python and SQL with experience in machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn for model development and deployment · Experience deploying machine learning models in production environments with knowledge ofCI/CD practices, DevOps methodologies, and cont…