Senior Manager / Manager, LLM Architect

Overview

The Consortium for Clinical Research and Innovation Singapore (CRIS), a wholly owned subsidiary of MOH Holdings, was established in 2020 with the goal of strengthening synergies and promulgating strategies for national-level clinical research and translation programmes under the stewardship of the Singapore Ministry of Health.

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)

• National Health Innovation Centre (NHIC)

• Advanced Cell Therapy and Research Institute, Singapore (ACTRIS)

• Precision Health Research, Singapore (PRECISE)

• Singapore Translational Cancer Consortium (STCC)

• 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. If you are as passionate as we are in clinical trials and research, we want you!

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 a senior LLM/Foundation Model architect to provide technical leadership for a healthcare foundation model programme in Singapore. The programme focuses on developing, adapting, evaluating, and preparing large language models for healthcare use cases, including clinical decision support, healthcare knowledge assistance, and domain-specific model adaptation.

This role will work closely with multiple project teams and partners to review, advise, and support their LLM development plans. The candidate will provide expert guidance across model selection, continual pretraining, fine-tuning, evaluation design, infrastructure planning, deployment feasibility, and engineering risk management.

The successful candidate should be able to combine strong LLM technical depth with practical engineering judgment. They should be comfortable reviewing technical proposals, advising teams on implementation approaches, supporting technical problem-solving, challenging assumptions constructively, and helping teams improve the quality, feasibility, and robustness of their LLM development work.

This is a technical leadership role rather than a day-to-day coding role. However, the candidate should have sufficient hands-on engineering background and practical LLM development experience to provide credible, concrete, and actionable guidance to project teams.

Healthcare domain experience is highly desirable. Or candidates with strong industry experience in LLM or foundation model development, adaptation, evaluation, or deployment in other domains are also preferred.

Key Responsibilities

1.LLM Technical Leadership, Advisory, and Oversight

• Serve as the programme-level technical lead for LLM architecture, model development strategy, training and tuning methodology, evaluation approach, and engineering feasibility.

• Review, advise, and support project teams on technical proposals covering model selection, continual pretraining, fine-tuning, evaluation design, infrastructure assumptions, deployment feasibility, and risk mitigation.

• Assess whether proposed methods are technically sound, feasible, scalable, reproducible, and aligned with programme objectives.

• Provide practical technical guidance to help teams improve their model development plans, experiment design, engineering approach, and technical outcomes.

• Establish review principles, reference approaches, and engineering guidelines for LLM development across the programme.

2.Support for Training, Tuning, and Evaluation Approaches

• Advise teams on suitable approaches for continual pretraining, domain-adaptive pretraining, supervised fine-tuning, instruction tuning, preference tuning, RAG, grounding, tool use, and other LLM adaptation methods.

• Review and support experiment design, baseline comparison, training stability, reproducibility, and model versioning, etc.

• Assess evaluation strategies covering task performance, clinical relevance, safety, hallucination risk, robustness, calibration, bias, and model limitations.

• Review model results and technical reports, and advise whether the evidence is sufficient for further development, pilot testing, or downstream deployment consideration.

3.Engineering and LLMOps Advisory

• Advise project teams on engineering approaches for model development, experiment tracking, evaluation pipelines, model lifecycle management, model serving, monitoring, and continuous iteration.

• Support teams in identifying scalable and maintainable engineering patterns, while working with dedicated implementation teams for actual delivery.

• Work with infrastructure, platform, data, and solution architecture teams to ensure technical assumptions are realistic and aligned across workstreams.

• Identify cross-cutting engineering risks related to scalability, reliability, observability, maintainability, cost, and operational feasibility.

• Support the definition of technical review gates, model readiness criteria, and engineering quality expectations.

4.Cross-Team Technical Alignment

• Facilitate technical design reviews, architecture discussions, model development reviews, and cross-team alignment sessions.

• Translate clinical, research, and business objectives into technical review criteria and actionable guidance for project teams.

• Communicate technical risks, trade-offs, and recommendations clearly to both technical and non-technical stakeholders.

• Support lightweight technical governance, including review processes, decision records, technical documentation, and milestone-level risk tracking.

• Ensure lessons learned, reusable patterns, and technical recommendations are shared across project teams.

Required Qualifications

1.Technical Expertise

• 5+ years of relevant experience in AI/LLM, machine learning, or large-scale technology architecture roles

• Proven experience with LLM or foundation model development, adaptation, evaluation, or deployment

• Strong understanding of modern LLM architectures, training methods, fine-tuning approaches, inference patterns, and model evaluation practices

• Practical knowledge of several of the following areas:

-continual pretraining or domain-adaptive pretraining

-supervised fine-tuning and instruction tuning

-preference optimization such as RLHF, DPO, GRPO, or related methods

-RAG, grounding, tool use, or agentic LLM systems

-LLM evaluation, safety testing, hallucination mitigation, and benchmarking

-distributed training, GPU infrastructure, model serving, or MLOps / LLMOps

-model lifecycle management, observability, reproducibility, and production readiness

• Ability to review technical designs, challenge assumptions, identify engineering risks, and provide actionable recommendations

• Strong engineering judgment across model quality, scalability, reliability, maintainability, cost, and operational feasibility

2.Architecture and Technical Leadership

• Experience providing technical leadership, architecture review, technical advisory, or engineering oversight across multiple teams, workstreams, or partner organizations

• Ability to guide and support technical teams without being the direct day-to-day implementer

• Strong ability to evaluate competing technical options and make pragmatic architecture recommendations

• Experience working with research, engineering, product, infrastructure, data, and stakeholder teams

• Strong written and verbal communication skills, including the ability to produce clear technical documentation, architecture notes, technical review comments, and decision records

Preferred Qualifications

• Industry experience developing, adapting, evaluating, or deploying LLMs or foundation models in production or near-production environments

• Experience advising, reviewing, or supporting LLM development work across multiple teams or partners

• Experience in healthcare AI, biomedical NLP, clinical decision support, medical foundation models, or healthcare data platforms

• Familiarity with healthcare data governance, privacy, security, clinical safety, and responsible AI practices

• Experience working with multi-partner, multi-vendor, or cross-institutional technology programmes

• Familiarity with Singapore’s healthcare ecosystem, public healthcare institutions, or national health technology programmes would be an advantage

What you need to know

Successful candidate will be offered a 3-year renewable contract. We regret that only shortlisted candidates will be contacted.

For more information about CRIS and the Business Entities, visit our websites below:

CRIS – https://www.cris.sg

SCRI – https://www.scri.edu.sg

NHIC – https://www.nhic.sg

ACTRIS – https://www.actris.sg

PRECISE – https://www.npm.sg

STCC – https://www.stcc.sg

CADENCE – https://www.cris.sg/our-programmes/cadence/

SIMFONI - https://www.cris.sg/singapore-medical-foundation-ai-model-simfoni

Please note that legitimate job openings at CRIS are published through CRIS’ official careers website and authorised recruitment channels. CRIS does not request payment or sensitive personal or financial information through unauthorised channels or individuals falsely claiming to represent CRIS. Candidates are advised to verify job opportunities through CRIS’ official channels. Thank you.