AI Engineer

Fullerton Healthcare Group Pte Ltd; Singapore; On-site; SGD 100,000 - 140,000; Full time

Job summary

Fullerton Health Group is seeking an AI Engineer to collaborate with AI/Data teams on cutting-edge AI and LLM technologies for healthcare challenges at scale. You will design, build, and productionize AI systems for document processing, automated medical-claim adjudication, and other AI-powered solutions across regions.

You will own the full model lifecycle—from data ingestion and finetuning to API delivery and GPU-optimized deployment—ensuring reliability, security, and performance across

Qualifications

• Bachelor’s degree or higher in CS/Math/Stats/Bioinformatics or related field.
• 1–3 years in AI/ML Engineer roles with production-ready experience.
• Strong Python skills for ML, data processing, and API/backend development.
• Hands-on experience with LLMs, prompts, RAG, embeddings, and evaluation.
• Proficiency with SQL/NoSQL and vector databases for embeddings and search.
• Awareness of data privacy, AI safety, and secure healthcare deployment.
• Experience with cybersecurity for AI/ML systems and secure network/API design.
• Excellent written and verbal communication; able to explain AI concepts to varied audiences.

Responsibilities

• Build, deploy and maintain OCR and Document AI pipelines to extract structured data from medical documents for downstream adjudication.
• Develop and optimise LLMs, including prompt engineering, inference tuning and cost/latency considerations; implement MLOps/LLMOps.
• Design retrieval-augmented generation (RAG) pipelines, embeddings, and agentic workflows for complex automation.
• Expose AI capabilities via secure Python APIs/services integrated with Fullerton Health systems.
• Ingest, cleanse and validate structured/unstructured data; build scalable batch/streaming data pipelines across regions.
• Design evaluation frameworks (offline/online) and monitor model performance, drift and latency with guardrails.
• Strengthen security posture: defend against model poisoning, prompt injection, and adversarial threats; harden APIs and cloud infra.
• Collaborate with product, backend, and ops to ship production-grade AI features and translate outputs into actionable insights.

Skills

Python

ML Engineering

Async

LLMs

Prompt Engineering

RAG Pipelines

Embeddings

Model Evaluation

SQL/NoSQL

Vector Databases

Data Privacy

Security Hardening

Communication

Education

Bachelor’s degree in CS/Math/Stats/Bioinformatics

Tools

PostgreSQL

MongoDB

Job description

About Fullert Health Group and Fullerton Health Technology (FHT)

Fullerton Health Group is a leading integrated healthcare provider across Asia-Pacific, operating an extensive network of clinics, medical centers, and healthcare services across multiple markets. Fullerton Health Technology (FHT) serves as the Group’s technology and innovation arm, spearheading enterprise AI transformation, data platform modernization, and intelligent healthcare automation initiatives across the region. Our mission is to re-architect healthcare operations through AI - spanning intelligent claims processing, document understanding, agentic workflow automation, and nextgeneration data platforms.

Role Summary

As an AI Engineer, you will work closely with the AI and Data teams to apply cutting-edge AI and LLM technologies to solve complex, real-world healthcare challenges at scale. You will have the opportunity to design, build, and productionize AI systems for document processing, automated medical-claim adjudication, and other AI-powered solutions.

You will own the full model lifecycle—from data ingestion and model adaptation (finetuning / RAG) to API delivery and GPU-optimized deployment-ensuring solutions are reliable, secure, and performant across multiple regions. You will also build and operate LLM-powered systems in production, with robust evaluation, monitoring, and guardrails in place.

Key Responsibilities

• Build, integrate and maintain OCR and Document AI pipelines (including multimodal models) that extract structured data from medical documents for downstream adjudication.
• Deploy, fine-tune and optimise large language models, including inference optimisation, cost/latency tuning, and model evaluation; implement best-practice MLOps / LLMOps
• Craft, test and optimise prompts; design and implement retrieval-augmented generation (RAG) pipelines, embeddings, and emerging agentic workflows for complex task automation.
• Expose AI capabilities via secure, scalable Python APIs/services and integrate them with existing Fullerton Health systems.
• Ingest, cleanse and validate structured and unstructured datasets; build robust, scalable data pipelines (batch/streaming) that synchronise across countries/regions.
• Design evaluation frameworks (offline/online), monitor model performance (accuracy, drift, hallucination, latency), and implement guardrails for safety, compliance, and data privacy.
• Strengthen the security posture of AI systems: defend against model poisoning, prompt injection, and adversarial attacks; harden APIs, networks, and cloud infrastructure; and support security reviews, threat modelling, and incident response for AI services.
• Work closely with product, backend, and ops teams to ship production-grade AI features; translate model outputs into clear, actionable insights for stakeholders.

Requirements

• Bachelor’s degree in Computer Sciences, Mathematics, Statistics, Bioinformatics or a related field.
• 1-3 years in an AI Engineer / Machine Learning Engineer (or similar) role, with experience building and shipping AI systems to production.
• Strong Python skills for ML, data processing, and API/backend development (experience with async, performance optimisation is a plus).
• Hands-on experience building applications with LLMs, including prompt engineering, RAG pipelines, embeddings, and model evaluation.
• Proficiency with SQL/NoSQL (e.g. PostgreSQL, MongoDB) and vector databases for embeddings and semantic search.
• Awareness of data privacy, AI safety, and secure deployment practices (especially for healthcare data).
• Exposure to cybersecurity for AI/ML systems is a strong plus: understanding of common attack vectors (e.g. model poisoning, prompt injection, data exfiltration, adversarial inputs), secure network and API design, and hands-on experience with security hardening, vulnerability assessment, or incident handling in cloud environment.
• Clear written and verbal communication; ability to explain complex AI concepts to technical and non-technical audiences