Lead AI Data Engineer

About Amplify Health
Who We Are
Amplify Health Asia Pte. Limited (Amplify Health) is a pan-Asian health technology and analytics company which brings together health technology assets, proprietary data analytics and healthcare expertise to support innovation across the healthcare value chain.
Headquartered in Singapore, Amplify Health operates in 10 markets across the region: Australia, Hong Kong, India, Indonesia, Malaysia, New Zealand, the Philippines, Singapore, Thailand and Vietnam.
Amplify Health develops integrated solutions that empower healthcare payors, providers and policymakers across Asia to identify value and drive new partnerships, understand variation and address systemic inefficiencies—with the aim of improving outcomes for individuals and the sustainability of health systems.
Powered by a clinically enriched data foundation, Amplify Health’s health intelligence platform combines modular AI solutions, context-aware automation and deep regional insights to offer localised, AI-powered health solutions spanning health payment integrity to chronic disease management. The company’s technology has earned recognition at several industry competitions, including the Celent Model Insurer Awards, ITC Asia Awards and SBR Technology Excellence Awards.
As an ISO27001 certified organisation, Amplify Health maintains clearly defined policies, structured oversight mechanisms and ongoing risk assessments that ensure alignment with global best practices on enterprise data security.
Our Vision and Ambition
To build the leading healthcare AI and platform services company in Asia that transforms the delivery of health and wellness for patients and communities by combining and leveraging the distinctive and complementary assets and strengths of AIA and Discovery.
Amplify Health will simplify access to health data and AI Innovation to accelerate distinct and disruptive healthcare value insights and resulting improvements in health outcomes through value-based care, personalised care plans and aligning individuals’ lifestyle/ behavioural choices.
By 2028, Amplify Health will have in place one of Asia’s strongest health-tech and AI capabilities; a comprehensive, integrated health technology stack supported by precision insights derived from proprietary data pools.
The Position
Role Overview
As a Lead AI Data Engineer, you will lead a small team that designs and delivers AI-ready data platforms and GenAI data solutions on Azure and Databricks. You stay hands-on, writing code, reviewing designs and unblocking the team, while owning the technical direction, delivery quality and people development for one or two projects. You will make the architecture trade-offs that decide whether AI solutions are accurate, secure, governed and cost-effective in production.
Role at a Glance
Experience: 9 to 12 years in data engineering, including 2+ years leading engineers and 2+ years delivering AI/ML or GenAI data solutions to production
Team Leadership: Leads a team of 3 to 6 engineers; may line-manage some. Accountable for technical delivery and team health
Project scope: Leads 1 to 2 projects at a time
Stakeholder / client exposure: Primary technical point of contact for project stakeholders and client teams; presents designs, demos and progress
Hands-on expectation: About 50 to 70% hands-on engineering
Responsibilities
Technical Leadership and Architecture
Own the end-to-end solution design for data and AI workloads on 1 to 2 projects: ingestion, lakehouse modelling, unstructured-data processing, retrieval, serving and evaluation.
Make and document architecture decisions (for example, as architecture decision records) on trade-offs such as batch versus streaming, Databricks Vector Search versus Azure AI Search, chunking and embedding strategy, model and endpoint selection, and build versus buy.
Define project engineering standards: coding conventions, testing strategy, CI/CD, infrastructure-as-code, environment promotion and release management.
Stay hands-on: build critical or complex components, prototype new patterns and lead deep-dive troubleshooting of production issues.
AI and GenAI Delivery
Lead delivery of RAG, agentic and ML data pipelines from proof of concept to production, including permission-aware retrieval, freshness service levels and fallbacks.
Set up evaluation and observability for AI solutions (golden datasets, offline and online evaluation, tracing, guardrails, quality dashboards) and use them as release gates.
Design how governed data is exposed to AI agents (MCP servers, tools and functions, semantic layers, Genie spaces or Fabric data agents) with the right access controls and human-in-the-loop checkpoints.
Manage AI cost and capacity: token budgets, model routing, caching, AI gateway policies and GPU / serverless sizing.
Governance, Security and Responsible AI
Ensure solutions meet data governance, privacy and security requirements: Unity Catalog / Purview, classification, masking and de-identification, lineage and audit.
Embed responsible-AI practices (risk assessment, data and model documentation, bias and quality checks, transparency) aligned to frameworks such as the NIST AI RMF, ISO/IEC 42001 and applicable regulations.
Work with information security and data protection teams on AI threat modelling, including prompt injection, data leakage and excessive agent permissions.
Delivery and Stakeholder Management
Translate business and client requirements into technical scope, estimates, plans and milestones; manage risks, dependencies and change.
Act as the main technical contact for project stakeholders; present designs, demos and progress to technical and non-technical audiences.
Work with product owners, data scientists, AI engineers, architects and platform teams to align priorities.
People Leadership
Lead a team of 3 to 6 engineers: allocate work, set clear goals, run code and design reviews, and remove blockers.
Coach and develop team members through regular feedback and development plans, and contribute to performance reviews.
Drive team adoption of AI-assisted engineering with clear guardrails (review standards, security, IP and data-handling rules) and measure its impact.
Take part in hiring through interviews, technical assessments and onboarding.
Candidate Profile
Required Qualifications
9 to 12 years in data engineering, with 2+ years leading a team or workstream of engineers.
Expert Python, PySpark and SQL, with strong system design and distributed data processing fundamentals.
Proven delivery of production lakehouse platforms on Azure and Databricks (Delta Lake, Unity Catalog, Lakeflow, streaming, CI/CD, infrastructure-as-code).
Proven delivery of at least two AI or GenAI data solutions to production (for example, RAG, document intelligence, agentic data access, feature / ML pipelines), including evaluation and monitoring.
Strong retrieval design knowledge: embeddings, chunking, hybrid search, re-ranking, index freshness and access control.
Experience with MLOps / LLMOps tooling (MLflow, model serving, AI gateway, prompt management) and AI evaluation methods.
Solid grasp of data governance, privacy, security and responsible-AI practices for sensitive data.
Experience planning and estimating work, managing delivery risk and communicating with senior stakeholders.
Demonstrated ability to coach engineers and to set and enforce engineering standards.
Technology stack: Primary stack (Azure + Databricks)
Cloud & storage: ADLS Gen2, Azure Data Factory, Event Hubs, Azure SQL, Cosmos DB, Key Vault, Microsoft Fabric / OneLake
Lakehouse & processing: Azure Databricks: Delta Lake, Lakeflow (Connect, Spark Declarative Pipelines, Jobs), Structured Streaming, Photon, serverless compute
Governance & catalog: Unity Catalog (lineage, tags, ABAC, row/column security), Microsoft Purview
AI, retrieval & agents: Databricks Vector Search, Model Serving, Agent Bricks, Lakebase; Azure AI Search; Microsoft Foundry (Azure OpenAI and other models, Agent Service); Azure AI Document Intelligence
MLOps / LLMOps: MLflow 3 (tracking, tracing, evaluation, prompt registry), Feature Store, Unity AI Gateway, inference tables
Engineering & DevOps: Python, PySpark, SQL, Git, GitHub Actions or Azure DevOps, Terraform / Bicep, Databricks Asset Bundles, Docker
AI-assisted engineering: GitHub Copilot, Databricks Genie Code / Assistant
Nice to have
Multi-agent systems and MCP server design; Databricks Agent Bricks or Microsoft Foundry Agent Service.
Microsoft Fabric and OneLake interoperability with Databricks.
Ontology, knowledge graph or semantic layer design for AI agents.
Experience in healthcare, insurance or other regulated industries; familiarity with HL7 FHIR and privacy regulations (for example, PDPA, HIPAA, GDPR).
Agile delivery certification or formal leadership training.

Work arrangement
Hybrid