Role Overview
We are seeking an experienced AI Architect to define and implement the enterprise AI architecture supporting Medical Smart Initiative (MSI) and Medical Analytics platforms. You will be responsible for designing the end-to-end AI lifecycle, establishing governance frameworks, and ensuring AI solutions are scalable, secure, and aligned with business and regulatory requirements.
This role is ideal for a senior AI professional with deep expertise in machine learning platforms, MLOps, and enterprise architecture.
Key Responsibilities
AI Architecture & Platform Design
- Define the target AI architecture across MSI and Medical Analytics platforms.
- Design the end-to-end AI lifecycle, including data preparation, model development, deployment, monitoring, and retraining.
- Establish reference architectures and reusable patterns for AI and generative AI solutions.
- Evaluate and recommend tools, frameworks, and cloud services to support enterprise AI initiatives.
AI Governance & Risk Management
- Define governance standards for model development, validation, approval, deployment, and retirement.
- Establish controls for explainability, bias detection, model drift monitoring, and auditability.
- Ensure compliance with security, privacy, and regulatory requirements.
- Collaborate with business, risk, and compliance stakeholders to operationalise responsible AI practices.
MLOps & Operational Excellence
- Define MLOps standards for CI/CD, automated testing, and model monitoring.
- Design deployment strategies for machine learning and generative AI workloads.
- Establish performance metrics and operational KPIs for AI systems.
Stakeholder Engagement
- Work closely with data scientists, data engineers, software engineers, and business stakeholders.
- Provide architectural guidance and technical leadership to delivery teams.
- Communicate architecture decisions and governance requirements to technical and non-technical audiences.
Required Skills & Experience
- 8+ years of experience in AI, machine learning, or data architecture roles.
- Proven experience designing enterprise AI/ML platforms and governance frameworks.
- Strong understanding of MLOps, model lifecycle management, and responsible AI principles.
- Experience with cloud-based AI platforms and services.
- Excellent communication and stakeholder management skills.
Technical Skills
- AWS SageMaker
- Amazon Bedrock
- Python
- ML frameworks (TensorFlow, PyTorch, Scikit-learn)
- CI/CD and Infrastructure as Code
- Model monitoring and governance tools
Preferred Experience
- Experience in healthcare, medical analytics, or regulated industries.
- Familiarity with generative AI, LLMs, and retrieval-augmented generation (RAG).
- Knowledge of data governance and enterprise architecture frameworks.
