Healthcare experience
Database management
system software, especially Microsoft SQL Server
Databases such as ADABAS, NoSQL, and cloud computing
Data mining and modelling
tools, especially ERWin,
Enterprise Architect, and Visio
Programming languages, e.g. Natural (SAG), Python, and
Java, as well as C/C++ and Perl
Operating systems: UNIX, Linux, Solaris, Windows, ZOS
AI and machine learning
frameworks (e.g. TensorFlow,
PyTorch, Scikit-learn)
Knowledge of AI solution design and architecture (including NLP, computer vision,
Skills:
Applied math and statistics
Data visualizatin and data migration
Machine learning
Attention to detail
Technical and Professional skills
Excellent problem-solving and analytical skills
Strong communication and interpersonal skills
AI model development and deployment (supervised, unsupervised, reinforcement learning)
Experience integrating AI models into business applications and workflows
Other requirements:
Leadership qualities
Ability to operate as part of a team to achieve a common goal
Positive attitude with a focus on achieving success
Take responsibility and accountability for assignments
Open to and supportive of new ideas
Qualifications :
BSc Computer Science or equivalent degree
At least 10 years of IT experience, with 8 years specifically in a data architect or data engineering role.
Proven experience in designing and implementing AI/ML solutions in enterprise environments.
Exposure to MLOps and AI model operationalization in either of the following platforms (e.g. Azure, AWS, or Google)
Data Architecture Design and Implementation:
Develop and maintain data models and database designs
Implement data integration and data warehousing solutions.
Ensure data architecture aligns with business and IT strategies.
Design and implement comprehensive data architecture solutions, including data models, data flows, and database designs
Lead the development and implementation of data integration, data warehousing, and big data solutions.
Machine learning and AI Design and Implementation:
Successfully deployed and maintained AI solutions aligned with business objective
Operationalised AI models with monitored performance
Established AI data pipelines and training workflows
Compliance with data privacy, ethical AI, and regulatory requirements
Data Quality and Security:
Establish and enforce data standards and policies.
Implement data quality and data governance practices.
Ensure data security and compliance with regulations.
Develop and maintain data standards, policies, and procedures to ensure data integrity, security, and compliance.
Collaboration and Communication:
Work closely with stakeholders to understand data requirements.
Provide technical leadership and guidance to the team.
Communicate effectively with business and IT teams.
Collaborate with business and IT teams to understand data requirements and translate them into effective data solutions.
Provide technical guidance and mentorship to junior data architects and other team members.
Performance and Scalability:
Ensure data systems are optimized for performance. Plan and implement scalable data solutions.
Monitor and address performance issues.
Ensure optimal performance, scalability, and reliability of data systems.
Stay updated with the latest industry trends and technologies in data architecture and recommend improvements.
Between 5 - 7 Years
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