AI Engineer Lead

Fullerton Healthcare Group; Singapore; On-site; SGD 100,000 - 150,000; Full time

Job summary

A leading company in healthcare is seeking an AI Engineer Lead to drive the technical direction and oversee a diverse team of AI engineers and data scientists. This role involves the design and deployment of advanced AI systems that enhance operational efficiency and quality of service. Candidates should have extensive experience in AI solutions, leadership capabilities, and a strong background in technical frameworks.

Qualifications

• 5+ years of experience in AI/ML solutions, ideally in a managerial role.
• Proven track record in AI project delivery, particularly in healthcare.
• Expert in Python and familiar with various MLOps tools.

Responsibilities

• Lead the design and optimization of AI systems across multiple regions.
• Recruit and mentor AI engineers and data scientists.
• Ensure compliance of AI solutions with healthcare regulations.

Skills

AI/ML solutions

Python

MLOps

leadership

document-AI projects

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.