Do meaningful work with us. Every day.
At Amplify Health, we’re looking for individuals with ambition, resilience and passion for healthcare, insurance, wellness and digital technology. As a fast-growing business with the ambition of making people and communities across Asia healthier, we have exciting career opportunities available to help us achieve our vision.
The AI Engineer will design, develop, deploy, and optimize AI-powered solutions leveraging Large Language Models (LLMs) and enterprise AI services including OpenAI, Azure OpenAI, Google Gemini, Azure Document Intelligence, AI Search, vector databases, and intelligent automation platforms. The role is responsible for building production-grade AI applications, implementing RAG (Retrieval-Augmented Generation) architectures, integrating AI services into business workflows, ensuring security and governance, and driving innovation through emerging AI technologies.
The successful candidate will work closely with product, engineering, business, and data teams to deliver scalable, secure, and high-impact AI solutions.
Responsibilities
1) Design and Develop AI & Generative AI Solutions
- Design, develop, and deploy AI applications using OpenAI, Azure OpenAI, Gemini, and other foundation models.
- Build RAG solutions using vector databases, embeddings, semantic search, and knowledge management platforms.
- Develop AI agents, copilots, chatbots, and workflow automation solutions.
- Engineer prompts, evaluation frameworks, and AI orchestration pipelines to improve model performance and reliability.
- Create APIs, microservices, and cloud-native architectures supporting enterprise AI use cases.
2) Implement Intelligent Document Processing & AI Services
- Develop document processing solutions using Azure Document Intelligence, OCR technologies, and other AI services.
- Extract, classify, validate, and transform structured and unstructured data from documents.
- Integrate AI services such as speech, vision, language understanding, translation, and content moderation into business applications.
- Design data pipelines to support AI training, retrieval, and analytics workloads.
- Ensure high-quality data processing and model outputs through robust validation frameworks.
3) AI Platform Engineering, Security & Governance
- Design scalable AI architectures on Azure and multi-cloud environments.
- Implement AI security, responsible AI practices, governance, compliance, and data privacy requirements.
- Monitor model performance, cost optimization, latency, hallucination mitigation, and solution reliability.
- Build CI/CD pipelines, MLOps/LLMOps practices, model deployment automation, and observability frameworks.
- Establish best practices for AI solution lifecycle management and enterprise adoption.
4) Innovation, Stakeholder Collaboration & Enablement
- Partner with business stakeholders to identify and prioritize AI opportunities.
- Provide technical leadership and recommendations on AI strategy, tooling, and emerging technologies.
- Conduct proof-of-concepts (POCs), pilots, and production implementations for AI initiatives.
- Stay current with advancements in GenAI, multimodal AI, AI agents, LLM frameworks, and industry trends.
Candidate Profile
Experience and Qualifications
- Over 5+ years of software engineering, cloud engineering, data engineering, or AI development experience, with at least 2+ years focused on AI/GenAI solutions.
- Demonstrated success in delivering enterprise-scale AI, machine learning, intelligent automation, or data-driven solutions.
- Proven experience with OpenAI, Azure OpenAI, Google Gemini, Anthropic Claude, or similar foundation models.
- Hands-on experience with Azure Document Intelligence, OCR solutions, and intelligent document processing platforms.
- Experience implementing RAG architectures, vector databases (Azure AI Search, Pinecone, Weaviate, ChromaDB, etc.), embeddings, and semantic search.
- Strong development experience in Python, REST APIs, SDK integration, and cloud-native application development.
- Experience with Azure services such as Azure AI Foundry, Azure AI Search, Functions, App Services, Container Apps, Kubernetes, Storage, and Key Vault.
- Knowledge of ML/LLM frameworks including LangChain, LangGraph, Semantic Kernel, LlamaIndex, AutoGen, or similar.
- Experience with DevOps/MLOps/LLMOps practices, GitHub, CI/CD, monitoring, and cloud security.
- Understanding of Responsible AI, model evaluation, governance, privacy, and compliance requirements.
- Bachelor’s degree in Engineering, Technology, Computer Science, Artificial Intelligence, Data Science. or related field required.
Competencies & Core Characteristics:
We are seeking a professional who embodies the following competencies and characteristics essential for success in our scale-up environment:
- Technical Domain Expertise: Possesses deep expertise across Generative AI, LLMs, AI agents, intelligent document processing, cloud AI services, and modern software engineering practices. Maintains current knowledge of rapidly evolving AI technologies and translates them into business value.
- Strategic Architect: Able to design scalable, secure, and future-ready AI solutions aligned with business objectives. Balances innovation with practical implementation considerations including cost, performance, governance, and maintainability.
- Unifier & Cross-Functional Influencer: Collaborates effectively across engineering, product, business, security, and operations teams. Influences stakeholders through technical credibility and communicates complex AI concepts in a clear and actionable manner.
- Data-Driven Decisiveness: Uses quantitative metrics, experimentation, evaluations, and operational insights to guide decisions. Continuously measures AI solution effectiveness and drives improvements based on data and business outcomes.
- Customer-Obsessed Advocate: Maintains a strong focus on delivering meaningful customer and business impact. Designs AI solutions that improve user experience, productivity, accuracy, and overall value realization.
- Resilient Operator: Thrives in rapidly evolving technology environments and adapts quickly to changing priorities. Demonstrates ownership, accountability, and persistence in solving complex technical challenges.
- Insatiable Curiosity: Continuously explores emerging AI trends, tools, models, and research developments. Challenges conventional approaches and actively identifies opportunities for innovation and competitive advantage.
Preferred Technical Skills Matrix
Must Haves:
Generative AI & LLM Platforms
- OpenAI / Azure OpenAI
- Google Gemini
- Prompt Engineering
LLM Application Development & Orchestration
- RAG (Retrieval-Augmented Generation) Architecture
- LangChain and/or Semantic Kernel
- REST API Integration
- Python
- Knowledge Retrieval & Search
- Azure AI Search / Vector Databases
- Azure Document Intelligence
Cloud & DevOps
- Azure Cloud Services
- GitHub, Version Control, and CI/CD Pipelines
Responsible AI, Security & Governance
- AI Security, Governance, and Compliance
- MLOps / LLMOps
Nice-to-Haves:
Agentic AI & Advanced Orchestration
- AI Agents / Agentic AI
- LangGraph
- LlamaIndex
Multi-Model & Advanced AI Solutions
- Anthropic Claude
- Multimodal AI (Vision, Speech, Video)
Machine Learning & Model Operations
- ML Model Development
- Fine-tuning and Model Evaluation
- MLOps / LLMOps
Cloud-Native Deployment
Enterprise AI Integration
Power Platform AI Integration
Knowledge Graphs
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