AI Engineer Lead

FULLERTON HEALTHCARE GROUP PTE LTD; Singapore; On-site; SGD 100,000 - 150,000; Full time

Job summary

A leading healthcare firm is seeking an AI Engineer Lead to spearhead AI initiatives, manage a multidisciplinary team, and enhance operational efficiency through innovative solutions. This role encompasses the end-to-end AI engineering function, ensuring compliance with healthcare regulations while driving the development of advanced AI systems. Candidates should have extensive experience in AI/ML, leadership skills, and technical proficiency in relevant tools and technologies.

Qualifications

• Experience in leading AI/ML projects in healthcare or related fields.
• Proven ability to translate complex AI concepts to non-technical stakeholders.
• Strong leadership experience in building high-impact teams.

Responsibilities

• Lead technical strategy and AI roadmap for healthcare applications.
• Oversee model lifecycle management and implement MLOps practices.
• Engage stakeholders to translate business requirements into technical deliverables.

Skills

Expert-level Python

Deep knowledge of LLM fine-tuning

Hands-on with MLOps tooling

Solid grasp of classical ML algorithms

Education

5+ years in AI/ML solutions

Tools

MLflow

Kubeflow

Docker

Kubernetes

Job description

Fullerton Health

Fullerton Health is scaling its AI practice to power next-generation medical-claim automation, advanced analytics, and patient-centric digital experiences across Asia. We are looking for an AI Engineer Lead who can set technical direction, grow a high-performing team, and deliver production-grade AI systems that materially improve our operational efficiency and service quality.

Role Summary

You will own the end-to-end AI engineering function—from vision and architecture to hands-on delivery and MLOps. Leading a squad of AI engineers and data scientists, you will steer the design, deployment, and continuous optimisation of Large Language Models (LLMs), OCR pipelines, and predictive services that process millions of medical documents across multiple regions.

Key Responsibilities

Area

What You’ll Lead

Technical Strategy & Architecture

Define the AI roadmap, technology stack, and reference architectures for OCR, LLM, and vector-search workloads; champion best practices in security, compliance, and scalability.

Team Leadership

Recruit, mentor, and inspire a multidisciplinary team (AI engineers, data scientists, MLOps). Set OKRs, run agile ceremonies, and create a culture of experimentation and rapid iteration.

Model Lifecycle Management

Oversee data ingestion, fine-tuning, evaluation, deployment, and monitoring of LLMs (e.g., Llama 3, Qwen) and vision models on GPU clusters.

Prompt & Retrieval Optimisation

Guide prompt-engineering initiatives, vector-database design, and RAG (Retrieval-Augmented Generation) patterns to maximise model accuracy and latency.

Production-Grade APIs

Architect and review Python services that expose AI capabilities to internal systems and partner apps with high availability and observability.

MLOps & DevSecOps

Implement CI/CD for models (GitOps pipelines, model registries), automate GPU provisioning, and enforce robust testing and rollback strategies.

Stakeholder Engagement

Translate business requirements from claims, operations, and compliance teams into technical deliverables; present AI performance metrics and ROI to senior leadership.

Governance & Compliance

Ensure AI solutions comply with healthcare regulations (PDPA, GDPR equivalents) and internal data-protection standards; lead model-risk assessments and audits.

Requirements

● Experience

○ 5 + years building and deploying AI/ML solutions, with 2 + years in a technical-lead or people-manager capacity.

○ Proven delivery of OCR or document-AI projects, ideally within healthcare, insurance, or fintech.

● Technical Proficiency

○ Expert-level Python; familiarity with Go/JavaScript a plus.

○ Deep knowledge of LLM fine-tuning (LoRA, QLoRA, PEFT) and GPU optimisation (CUDA, Triton, TensorRT).

○ Hands-on with MLOps tooling (MLflow, Kubeflow, SageMaker, or similar) and container orchestration (Docker, Kubernetes).

○ Production experience with vector stores (Faiss, Milvus, pgvector) and distributed databases (PostgreSQL, MongoDB, Redis).

○ Solid grasp of classical ML algorithms and statistical learning theory.

● Leadership & Communication

○ Track record of building diverse, high-impact teams and fostering a culture of continuous learning.

○ Ability to convey complex AI concepts to executives and non-technical stakeholders.

○ Strong project-management skills; comfortable balancing roadmap priorities against resource constraints.

● Nice to Have

○ Exposure to cloud-cost optimisation and GPU fleet management.

○ Knowledge of security frameworks (SOC 2, ISO 27001) and healthcare-data compliance.

○ Experience with multilingual NLP (Vietnamese, Bahasa, Chinese) and low-resource language adaptation.

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