The key focus for the
senior data/AI architect is to perform planning aligned to key AI solutions,
build and participate in the architecture capability building, perform AI
architecture and design, manage AI architecture risk and compliance, provide
design and build governance and support and communicate and share knowledge
around the architecture practices, guardrails, blueprints and standards related
to the AI solution design.

A key focus of this role is
partnering with the AI Technology Centre of Excellence to build out the
organisation's Databricks AI platform and support the delivery of enterprise AI
and generative AI use cases.

Planning

• 
  Lead AI solution
  requirements gathering and ensure alignment with business objectives and
  constraints.
• 
  Define and refine AI
  architecture runways for intentional architecture with the key
  stakeholders
• 
  Provide input into
  business cases and costing
• 
  Participate and
  provide AI architectural runway requirements into Programme Increment (PI)
  Planning

Architecture Capability

• 
  Design and implement enterprise-grade AI
  architectures leveraging Databricks and cloud-native technologies.
• 
  Develop and
  oversee AI architecture views and ensure alignment with enterprise
  architecture.
• 
  Maintain and oversee
  the AI solution artifacts in the set enterprise repository and knowledge
  portals aligned to the rest of the architecture
• 
  Manage the AI
  architecture processes based on the requirements for each architype
• 
  Manage change impact
  of the AI architecture with stakeholders
• 
  Develop and
  participate in the build of the AI architecture practice with embedded
  architects and engineers including the relevant methods, repository and
  tools
• 
  Manage the AI
  architecture considering the business, application, information/data and
  technology viewpoints
• 
  Establish, enforce and
  implement AI standards, guardrails, frameworks, and patterns
• 
  Partner with the AI
  Tech COE to define and evolve the Databricks AI platform architecture,
  ensuring alignment with enterprise data and AI strategy
• 
  Design and implement
  AI/ML architectures on Databricks, including MLOps pipelines, model
  lifecycle management, Unity AI Gateway and Unity Catalog governance for
  AI/ML assets

Solution Design

• 
  Lead and
  review logical and detailed AI architecture
• 
  Evaluate and
  approve AI solution options and technology selections
• 
  Select appropriate
  technology, tools and build for the solution
• 
  Oversee and
  maintain the AI solution blueprints
• 
  Drive incremental
  modernisation initiatives in the delivery area
• 
  Design and evaluate
  architectures for AI and generative AI use cases, including RAG pipelines,
  vector stores, feature stores, and LLM integration patterns

Risk, Governance and Compliance

·
Identify, assess and mitigate risks at a AI solution architecture
level

·
Ensure and enforce compliance with policies, standards, and
regulations

·
Lead AI architecture reviews and integrate with governance
functions

·
Integrate with other governance and compliance functions to ensure
continuity in managing the investment and risk for the organisation pertaining
to the solution architectures

·
Establish and provide AI standards, guidance, and tools to delivery
teams.

Implementation and Collaboration

·
Establish and provide AI solution architectures and tools to the
delivery and AI engineering teams

·
Lead and facilitate collaboration with delivery teams to achieve
architecture objectives

·
Manage and resolve deviations and ensure up-to-date AI solution
design documentation

·
Identify opportunities to optimise delivery of solutions

·
Oversee and conduct post-implementation reviews

·
Ensure the AI architecture supports CI/CD pipelines to facilitate rapid
and reliable deployment of data solutions

·
Implement automated testing frameworks for AI solutions to ensure
quality and reliability throughout the development lifecycle.

·
Establish performance monitoring and optimisation practices to ensure AI
solutions meet performance benchmarks and can scale as needed.

·
Integrate robust AI security measures, including encryption, access
controls, and regular security audits, into the implementation process.

Communication and Knowledge Sharing

·
Communicate and advocate up-to-date AI solution architecture views

·
Communicate the relevant AI standards, practices, guardrails and tools
to stakeholders relevant to the solution design

·
Ensure IT teams are well-informed and trained in architecture
requirements

·
Communicate and collaborate with stakeholders' relevant views on
planning, technology assessments, risk, compliance, governance and
implementation assessments

·
Foster collaboration between AI architects, AI
engineers, and other IT teams through regular cross-functional meetings and
agile ceremonies.

·
Communicate and maintain up-to-date blueprint designs for key data
solutions

·
Ensure effective participation in the agile ceremonies (PI planning,
sprint planning, retrospectives, demos)

·
Implement regular feedback loops with stakeholders
and end-users to continuously improve data solutions based on real-world usage
and requirements

·
Create a culture of knowledge sharing by organising
regular workshops, training sessions, and documentation updates to keep all
team members informed about the latest AI architecture practices and tools

Requirements

MINIMUM
QUALIFICATIONS/EXPERIENCE

• 
  Matric
• 
  Degree or diploma in
  Information Technology, Computer Science, Engineering OR relevant diploma
  / degree
• 
  Experience:
  Requires a minimum of
  5 years in a technical/solution design role and a minimum of 7 years
  relevant IT experience
• 
  Data and AI
  Experience:
  Required a minimum of 7 years related experience in AI,
  data engineering, data modeling and design and data management and
  governance
• 
  Expert-level proficiency in Databricks, including
  Delta Lake, Spark, and MLflow.

·
Proven
experience architecting and delivering AI/ML solutions on Databricks, including
MLOps, model deployment and monitoring, and Unity Catalog governance for AI/ML
assets.

·
Hands-on
experience in large-scale data and AI platform implementation (preferably
cloud-based).

ADDITIONAL
QUALIFICATIONS/EXPERIENCE (PREFERRED, NOT A REQUIREMENT)

• 
  DAMA-DMBOK
• 
  TOGAF
• 
  ArchiMate
• 
  Cloud Certifications
  (AWS, Azure)
• 
  Financial Industry
  Experience
• 
  Certifications in Databricks, AWS ML, AWS Data
  Engineering or similar.
• 
  Experience with
  generative AI / LLM architectures (e.g. RAG pipelines, vector databases,
  AI gateways)
• 
  Databricks Certified
  Machine Learning Professional or equivalent certification

Data
Related Experience:

·
Big Data and Analytics (e.g., Hadoop, Spark)

·
Data Warehousing

·
Master Data Management (MDM)

·
Data Lakes, Lakehouse, and Data Mesh

·
Metadata Management

·
ETL/ELT Processes

·
Data Privacy and Compliance

• 
  Cloud Data Services
• 
  Experience with AI cloud platforms (Azure, AWS,
  or GCP) and associated data services.

·
Proficiency in
SQL, Python, and distributed data processing frameworks.

·
Familiarity with
CI/CD for data pipelines and DevOps practices.

·
Experience with
Lakehouse architecture and real-time streaming solutions

Related
attributes and competencies related to architecture:

·
Critical thinking/problem solving

·
Teamwork/collaboration

·
Effective Communication Skills

·
Leadership skills

·
Knowledge and experience in architecture domains

·
Knowledge and experience in architecture methods, frameworks and tools

·
Solution Design Experience

·
Agile Knowledge and Experience

·
Cloud Knowledge and Experience

AI
related competencies:

·
AI architecture principles and methodologies

·
AI integration technologies and tools

·
AI management and governance

·
AI/ML architecture, MLOps, and model lifecycle management knowledge and
experience