Overview
Manager – Data Architecture AI Jobs in Johannesburg Metropolitan Area at Samaha Consulting
Title: Manager – Data Architecture AI
Company: Samaha Consulting
Location: Johannesburg Metropolitan Area
The AI Architect will be accountable to achieve the following objectives:
- Define and own the enterprise AI architecture, including ML, GenAI, and intelligent automation platforms
- Translate business and product strategies into AI solution blueprints and reference architectures
- Evaluate and select AI technologies, frameworks, and vendors aligned to strategic goals
- Design end‑to‑end AI solutions, from data ingestion and feature engineering to model deployment and monitoring
- Ensure AI solutions are production‑grade, scalable, observable, and resilient
- Guide engineering teams on AI solution design, deployment patterns, and best practices
- Establish AI governance frameworks, including model lifecycle management, explainability, fairness, and auditability
- Ensure compliance with regulatory, security, privacy, and ethical AI standards
- Define model risk management and approval processes
- Architect AI platforms integrated with data platforms, APIs, cloud services, and enterprise systems
- Support MLOps and LLMOps capabilities including CI/CD, monitoring, drift detection, and retraining
- Align AI architecture with enterprise data, cloud, and security architectures
- Partner with business leaders, product teams, data scientists, engineers, and architects
- Provide technical leadership and mentorship across AI and data teams
- Act as the AI subject‑matter expert for strategic initiatives
Governance
- Lead, set up / participate in planned and adhoc meetings for Fintech AI strategy and capability roadmaps
- – Drive enterprise-wide AI transformation initiatives, elicit inputs from relevant parties
- – Drive adequate risk mitigation and controls, and elicit inputs from relevant parties
- – Prepare proposal on change initiatives, SLA policies and procedures for AI solutions and strategy across the group
- – Sign off approval on changed and new initiatives for any change to the data architectural environment
Escalations
- Manage and provide solutions to escalations that have impact on FinTech AI and processes
Reporting
- Report on a periodic basis to the function leadership on progress made within the function and in accordance with the measurement metrics set by the organization
- Report on an ad hoc basis on specific projects, as required
Education:
- Bachelor’s degree in Computer Science, Information Systems, Data Management, or related technical field
- Relevant certification/accreditation/membership with professional body as required
Experience:
- 5+ years’ experience in AI, data or solution architecture roles
- Proven experience delivering enterprise‑grade AI solutions
- Experience working in large-scale, enterprise or regulated environments
- Experience with cloud data platforms and big data ecosystems (Advantageous)
- Prior experience Financial Services preferred, with experience within the banking/big data/ telecom industry and with Mobile Money being an added advantage
- Experience in Integration & Orchestration, Functional Architecture Design, Technical Solution Design
- Demonstrated ability to architect end‑to‑end AI solutions with embedded Responsible AI principles, including fairness, explainability, transparency, privacy, security, and model governance, as well as quality assurance and validation methodologies.
- Proven capability to work effectively with both structured and unstructured data across the full AI lifecycle.
- Worked across diverse cultures and geographies
Competencies:
Functional Knowledge:
- Strong AI Solution Architecture and ability to design end‑to‑end, enterprise‑scale AI solutions, integrating data, models, platforms, and business processes
- Strong understanding of LLMs, AI agents, Retrieval‑Augmented Generation (RAG), prompt design, and orchestration patterns
- Responsible AI & Ethics with in-depth knowledge of fairness, explainability, bias mitigation, transparency, privacy, security, and regulatory compliance, embedded throughout the AI lifecycle
- Expertise in model evaluation, testing, monitoring, drift detection, and performance validation in production environments
- MLOps / LLMOps – Practical experience with CI/CD for AI, model deployment, retraining, observability, and lifecycle management
- Strong capability to work with structured, semi‑structured, and unstructured data, including feature engineering and data readiness for AI
- Ability to assess and manage model risk, data risk, and operational risk associated with AI solutions
- Cloud & Platform Expertise