FM Architect

The Consortium for Clinical Research and Innovation, Singapore (CRIS) brings together seven national R&D, clinical translation and service programmes to advance clinical research and innovation for Singapore, and establish important capabilities for a future-ready healthcare system. The Business Entities under CRIS include: Singapore Clinical Research Institute (SCRI)Advanced Cell Therapy and Research Institute, Singapore (ACTRIS)Cardiovascular Disease National Collaborative Enterprise (CADENCE)Singapore Medical Foundation AI model (SIMFONI)Together, CRIS makes a positive difference to Singapore patients and researchers by ensuring that these clinical research platforms and programmes are at the cutting edge of capability development and innovation. Overview of SIMFONI The SIngapore Medical FOundation AI model (SIMFONI) Programme was established in 2025 to advance the safe and responsible use of Artificial Intelligence (AI) in Singapore’s public healthcare ecosystem to support healthcare professionals in providing care to patients. SIMFONI will build up the capabilities and infrastructure to accelerate the development and deployment of large-scale machine learning models, known as Foundation models (FM) for improved healthcare outcomes. About the Role We are hiring an experienced Foundation Model Architect and Engineer for the technical execution of SIMFONI projects which focus on training, building and deploying a Large Language Model (LLM) for Healthcare in Singapore. This role involves end-to-end oversight of the engineering work of LLM model development and deployment, focusing on LLM architecture and training while overseeing data architecture and pipelines, infrastructure and high-performance computing, as well as the model testing and evaluation. Key Responsibilities Setting up the principle and guideline for the technical direction for the foundation model architecture, training, fine-tuning, and evaluation strategies. Overseeing the integration architecture of clinical data sources as well as healthcare systems for continuous model development and outcome delivery Monitoring the progress and evaluating the outcomes of various workstreams, while overseeing the overall engineering efforts across the programme Data architecture & engineering (data curation, data pipelines, annotation, quality control)Infrastructure & high-performance compute resource planning, provisioning and distributed training environments management Additionally, the role will be responsible for: Implementing a lightweight agile framework to manage engineering progress across distributed and cross-organizational teams. Facilitating regular standup, planning sessions, retrospectives, and cross-team alignment meetings for engineering efforts and serving as the focal for engineering reporting and iteration. Tracking tech deliverables, dependencies and risks using agile tooling (e.g. Jira). System Integration and Deployment Overseeing the integration of the trained LLM into clinical systems and digital platforms Managing engineering planning for APIs, data ingestion, output delivery and downstream use cases Ensuring scalability, monitoring, and model reliability in production environments Security, Privacy & Compliance Ensuring engineering work meets standards for security, data privacy and other compliance requirements in Singapore Healthcare. Collaborating with data & security authorities to design privacy-preserved data pipelines and secure deployment and operation strategies. Stakeholder Engagement & Technical Representation Acting as the central technical point of contact across internal leadership and external partners. Translating business and clinical requirements to technical strategies, guidelines and action plans. Managing clear and consistent documentations and records for engineering practices and workloads. Required Qualifications At least 6 years’ experience in engineering leadership roles, ideally in Artificial Intelligence, Machine Learning or data-focused area. Proven experience with LLM or foundation model development, including architecture and training. Practical understanding of evaluation and validation workflows for LLM models and modern LLM/ML development tools and stacks. Preferred Qualifications Experience in AI/ML or other tech development and deployment in healthcare, especially Singapore public health sectors. Familiarity with healthcare tech standards, governance and clinical safety practices. Experience managing multi-vendor or multi-partner engineering programs. #J-18808-Ljbffr