AI Strategy & Solution Design
- Assessed business opportunities and identified areas where AI technologies could drive measurable value and operational efficiency.
- Defined AI use cases, business value propositions, and implementation roadmaps aligned with strategic objectives.
- Designed enterprise AI architectures aligned with cloud, security, and organisational standards.
- Developed technical solutions leveraging Generative AI (GenAI), Machine Learning (ML), Retrieval Augmented Generation (RAG), Agentic AI, Natural Language Processing (NLP), and Intelligent Document Processing (IDP).
- Established AI governance frameworks, security standards, model risk controls, and responsible AI practices.
Deliverables:
- AI Opportunity Assessments
- AI Use Case Catalogues
- Business Cases and Value Propositions
- Solution Architecture Documents
- Technical Design Documentation
AI Platform Engineering
- Designed, built, and maintained scalable enterprise AI platforms capable of supporting production-grade AI workloads.
- Configured and managed cloud-native AI services and infrastructure.
- Established AI development environments and engineering standards.
- Implemented model registries, feature stores, and model lifecycle management capabilities.
- Developed Infrastructure-as-Code (IaC) solutions to automate AI deployments.
- Implemented MLOps practices to streamline development, deployment, monitoring, and governance.
Deliverables:
- AI Platform Deployments
- MLOps Frameworks
- CI/CD Pipelines for AI Solutions
- Infrastructure Automation Scripts
- Environment Build and Configuration Documentation
Generative AI Engineering
- Designed and developed enterprise Generative AI applications and intelligent automation solutions.
- Built Copilot-style AI assistants, virtual agents, and conversational AI platforms.
- Implemented Retrieval Augmented Generation (RAG) solutions to enhance the accuracy and relevance of Large Language Model responses.
- Developed Agentic AI workflows capable of autonomous decision-making and task orchestration.
- Integrated Large Language Models (LLMs) with enterprise systems, applications, and business processes.
- Engineered prompts, orchestration layers, and workflow automation to optimise AI solution performance and user experience.
Deliverables:
- Enterprise AI Assistants
- Copilot Solutions
- RAG-Based Applications
- Agentic AI Workflows
- Prompt Engineering Libraries
- AI and Enterprise System Integrations
Machine Learning Engineering
- Developed predictive, classification, recommendation, and analytical models to support business decision-making.
- Designed and implemented end-to-end model training pipelines.
- Performed feature engineering, data transformation, and model optimisation activities.
- Conducted model testing, validation, benchmarking, and performance tuning.
- Managed model deployment, versioning, and production lifecycle management.
Deliverables:
- Production-Ready Machine Learning Models
- Automated Training Pipelines
- Feature Engineering Frameworks
- Model Evaluation and Validation Reports
- Model Performance Dashboards
Data Engineering for AI
- Designed and implemented AI-ready data architectures and pipelines.
- Enabled ingestion and processing of structured, semi-structured, and unstructured data sources.
- Established data quality management processes and controls.
- Implemented and optimised vector databases to support GenAI and RAG solutions.
- Prepared, transformed, and managed datasets used for model training, validation, and inference.
Deliverables:
- Data Ingestion and Processing Pipelines
- Vector Database Implementations
- Data Quality Frameworks
- Metadata Catalogues
- Data Lineage and Traceability Documentation
AI Operations (AIOps & MLOps)
- Monitored the performance, reliability, and health of AI solutions in production environments.
- Identified and managed model drift, degradation, and performance issues.
- Managed model retraining schedules and continuous improvement initiatives.
- Provided incident investigation, troubleshooting, and operational support for AI solutions.
- Developed operational dashboards and monitoring frameworks to ensure solution stability and transparency.
Deliverables:
- AI Monitoring Dashboards
- Model Drift and Performance Reports
- AI Incident Management Reports
- Model Retraining Plans
- Operational Runbooks and Support Documentation
AI Governance, Risk & Compliance
- Implemented Responsible AI controls and ethical AI practices across enterprise solutions.
- Ensured compliance with regulatory requirements, including POPIA and internal governance frameworks.
- Conducted AI risk assessments and model risk evaluations.
- Maintained audit trails, evidence repositories, and end-to-end traceability for AI systems.
- Supported internal and external audits, compliance reviews, and governance reporting activities.
Deliverables:
- Responsible AI Assessments
- AI and Model Risk Assessments
- Audit Evidence Packs
- Governance and Compliance Reports
- Regulatory Compliance Registers
Core Skills & Expertise
- Artificial Intelligence (AI)
- Generative AI (GenAI)
- Machine Learning (ML)
- Retrieval Augmented Generation (RAG)
- Agentic AI
- Natural Language Processing (NLP)
- Intelligent Document Processing (IDP)
- Large Language Models (LLMs)
- Prompt Engineering
- AI Architecture & Solution Design
- Data Engineering
- Vector Databases
- MLOps & AIOps
- Cloud AI Platforms
- CI/CD & Infrastructure as Code (IaC)
- AI Governance, Risk & Compliance
- Responsible AI & Ethical AI Practices
- Enterprise Systems Integration
- Model Development, Deployment & Monitoring