AIML Engineer

AI/ML Engineer

Purpose:

Serve as the technical backbone of the AI Hub. Design build productionise and maintain core AI/ML infrastructure and reusable components that will power the DT2.0 platform build priority use cases and the Groups intelligent transformation.

Key Responsibilities:

• Design and implement core AI infrastructure model serving MLOps pipelines RAG architectures vector databases DT Data Lake integration rules engine and microservices.

• Lead technical delivery of high-impact use cases AI-Assisted Legacy Modernisation Intelligent Operations and AI-native components of DT2.0.

• Build and maintain the foundational AI Agent Layer and reusable AI services across onboarding operations and sales enablement including multi-agent orchestration.

• Apply AI/ML techniques to accelerate the DT2.0 platform build (automated code analysis intelligent configuration generation predictive monitoring).

• Implement production-grade evaluation observability and monitoring eval harnesses regression testing A/B testing of models continuous quality measurement.

• Own LLM cost and latency optimisation model selection caching prompt engineering token economics and routing logic.

• Establish AI security standards prompt injection defence output validation secrets management model access controls to fintech-grade requirements.

• Ensure all solutions are modular scalable cloud-native secure and fully compliant with the Responsible AI Policy PCI-DSS and POPIA.

• Mentor squad members and contribute to AI standards and best practices across the organisation.

• Track and report technical KPIs and ROI of AI initiatives.

Requirements & Qualifications:

Essential

• BSc Computer Science or equivalent technical degree non-negotiable.

• 36 years software engineering experience with AI/ML in production.

• Strong C#/.NET proficiency (primary API stack) shipping production APIs.

• Python proficiency for AI/ML workloads.

• Modern AI/ML frameworks: LangChain LlamaIndex Hugging Face OpenAI/Anthropic APIs scikit-learn PyTorch/TensorFlow.

• RAG systems with vector DBs (Pinecone Weaviate pgvector Qdrant).

• Agentic / multi-agent systems (LangGraph CrewAI AutoGen custom orchestration).

• MLOps Azure (preferred) Docker/Kubernetes data pipelines.

• Azure DevOps (primary) for CI/CD.

• Passion for clean production-grade code.

Bonus / Advantageous

• FastAPI for AI service-specific work.

• Eval / observability tooling LangSmith Langfuse Arize W&B.

• Fine-tuning approaches PEFT LoRA QLoRA and trade-off judgement.

• GitHub Actions or other modern CI/CD tooling.

• Modern data engineering dbt Spark/Databricks Kafka.

• Fintech/payments or large-scale system modernisation background.

What Success Looks Like in 12 Months:

• Core AI infrastructure is live and reused across multiple DT2.0 squads.

• Two priority use cases (Legacy Modernisation Toolkit and Operations layer) are in production.

• Measurable acceleration of DT2.0 delivery velocity visible to EXCO.

• AI services in production with defined SLAs eval coverage and cost/latency baselines.