AI​/ML Engineer

AI/ML Engineer Purpose

Serve as the technical backbone of the AI Hub. Design, build, product ionise and maintain core AI/ML infrastructure and reusable components that will power the DT2.0 platform build, priority use cases, and the Group's intelligent transformation.

• 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.

• BSc Computer Science or equivalent technical degree — non‑negotiable.
• 3–6 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:
  Lang Chain, Llama Index, Hugging Face, OpenAI/Anthropic APIs, scikit‑learn, PyTorch/Tensor Flow.
• RAG systems with vector DBs (Pinecone, Weaviate, pgvector, Qdrant).
• Agentic / multi‑agent systems (Lang Graph, CrewAI, Auto Gen, custom orchestration).
• MLOps, Azure (preferred), Docker/Kubernetes, data pipelines.
• Azure Dev Ops (primary) for CI/CD.
• Passion for clean, production‑grade code.

• FastAPI for AI service‑specific work.
• Eval / observability tooling — Lang Smith, Langfuse, Arize, W&B.
• Fine‑tuning approaches — PEFT, LoRA, QLoRA, and trade‑off judgement.
• Git Hub Actions or other modern CI/CD tooling.
• Modern data engineering — dbt, Spark/Databricks, Kafka.
• Fintech/payments or large‑scale system modernisation background.

• 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.