Machine Learning Data Engineer – Generative AI at IQbusiness

Job Description

• We are seeking an experienced AI/ML engineer to join our dynamic merchant payments team in Stellenbosch/Cape Town. This is a hybrid role requiring in-office presence three days per week.
• As part of a collaborative and high-performing team, you will design, build, and productionise secure, scalable AI/ML solutions with a strong focus on merchant payments and financial transaction processing. Working at the intersection of data engineering and machine learning, you’ll contribute to projects that demand innovation, compliance, and technical excellence.
• If you thrive on solving complex challenges, enjoy working alongside motivated professionals, and want to deliver AI systems that make a real impact, this role is for you.

Key responsibilities and output areas include:

LLM Solution Engineering

• Architect and implement RAG pipelines over payments and merchant data (tickets, chargebacks, settlement statements, documentation).
• Fine-tune and evaluate domain-adapted models (prompting, adapters/LoRA, retrieval, re-ranking, and guardrails).
• Deploy secure, observable model services behind internal APIs with SLA/SLOs and A/B or canary rollout.

Data Engineering for AI

• Build events drive pipelines (Kafka) and ELT into feature/embedding stores; manage data contracts, schema evolution, and quality gates end to end.
• Optimise storage/layouts and retrieval for vector indexes and low-latency use cases.

MLOps & Reliability

• Implement training/inference CI/CD, model/version registries, dataset lineage, drift/guardrail monitoring, red teaming, and safety filters.
• Define offline and online evaluation (factuality, toxicity, PII safety, and business KPIs such as deflection, turnaround time, and fraud catch uplift).

Security & Compliance

• Engineer solutions consistent with PCI DSS expectations when handling card data (segmentation, least privilege, logging/monitoring, encryption, and vulnerability management).
• Partner with security, risk, and compliance teams for design reviews.

Collaboration & Delivery

• Partner with Merchant Ops, Risk/Fraud, Disputes, Engineering, and Legal teams to translate problem statements into shipped AI features.
• Contribute to AI governance (model cards, usage policies, evaluation packs).

Key Requirements

• 6+ years in data or ML engineering, with at least 2+ years hands on in LLMs / modern NLP (RAG, vector search, embeddings, prompt engineering, fine-tuning).
• Proven experience in production deployments of LLM-backed services (latency, cost, monitoring, rollback).
• Technical Skills
• Strong proficiency in Python (data + serving) and SQL.
• Experience with event streaming (Kafka) and workflow orchestration (Airflow/Dagster).
• Cloud expertise (AWS): containerised workloads, infrastructure as code (Terraform), and Kubernetes (Rancher-managed environments acceptable).
• Experience building and operating data pipelines (batch + streaming), dimensional models, and feature/embedding stores.

Domain Knowledge

• Ability to design solutions aligned with PCI DSS controls (or willingness to upskill rapidly).
• Payments/merchant domain exposure (chargebacks, disputes, settlements, reconciliation, 3DS, fraud signals) is advantageous.
• Soft Skills
• Strong problem-solving and analytical ability.
• Ability to work effectively in agile teams.
• Clear communication with technical and non-technical stakeholders.

Education

• Bachelor’s degree in computer science, information systems, or a related field.
• Relevant certifications in AI/ML, cloud, or payment systems are advantageous.

Nice to Have

• Experience with vector databases (e.g., OpenSearch k NN, pgvector, Pinecone), rerankers, safety/guardrail frameworks.
• Observability for AI: prompt/version telemetry, evaluation pipelines, and human-in-the-loop feedback.
• FinOps mindset: model/runtime cost optimisation at scale on AWS.
• Exposure to performance tuning, observability tools, or A/B testing.

Closing Date 13 March 2026