Machine Learning Engineer

ABOUT THE OPPORTUNITY

Our client is a pioneer in decision-making AI, applying reinforcement learning, deep learning, and large-scale machine learning to solve real-world problems across industries including logistics, biotech, finance, and enterprise digital transformation. Backed by deep research partnerships and a global footprint, they bring together scientists, engineers, and researchers to build next-generation AI systems for large-scale, high-stakes problems. As they continue to scale their capabilities and deepen their footprint in South Africa, they are looking for a Machine Learning Engineer to join a research-driven, high-impact team.

This is a hybrid research-and-engineering role: you'll work at the intersection of applying state-of-the-art ML research and shipping it into production systems that create measurable, real-world impact: from fraud detection and revenue optimisation to next-generation, agentic AI-powered customer experiences.

WHAT YOU'LL DO

• Design, train, and deploy machine learning models across supervised, unsupervised, deep learning, and reinforcement learning paradigms to solve high-stakes decision-making problems


• Build and optimise Large Language Model (LLM) solutions using GPT, BERT, and open-source alternatives: including RAG pipelines, prompt engineering, PEFT, and LoRA fine-tuning


• Develop Agentic AI systems and autonomous decision-making agents that integrate seamlessly with legacy infrastructure


• Work with distributed, GPU-accelerated infrastructure to train and scale models efficiently


• Architect and maintain ETL pipelines, data lakes, and feature engineering workflows that power model training and inference at scale


• Collaborate closely with data engineers, researchers, and platform teams to deploy models via Docker, Kubernetes, and cloud platforms (AWS, Azure AI Foundry, Azure ML Studio)


• Translate research prototypes into robust, production-grade systems, working across the full MLOps lifecycle: from experimentation to deployment and monitoring


• Drive measurable business impact through predictive modelling, customer segmentation, fraud detection, and rigorous A/B testing frameworks


• Contribute to internal knowledge-sharing and mentoring, helping build technical depth across the team

WHAT YOU'LL NEED

Core AI & ML

• Proven expertise across the ML spectrum: supervised/unsupervised learning, deep learning, reinforcement learning, generative AI, NLP, GANs, and transfer learning


• Hands-on experience with LLMs and GenAI frameworks — LangChain, LangGraph, LangServe, Hugging Face, OpenAI APIs


• Strong grounding in vector databases and retrieval-augmented generation (RAG) architectures, including FAISS


• Comfort operating at the intersection of research and engineering — able to read papers, prototype ideas quickly, and take them to production

Programming & Tooling

• Python (primary), with working knowledge of SQL, Java, or Scala


• Proficiency with ML frameworks: TensorFlow, PyTorch (JAX exposure a strong plus)


• API and application development: FastAPI, Streamlit

Big Data & MLOps

• Experience with distributed data processing: Apache Spark, Hive, PIG


• Workflow orchestration via Airflow


• Containerisation and deployment: Docker, Kubernetes


• Cloud-native ML: AWS and/or Azure AI Foundry / Azure ML Studio

Data Engineering

• Solid understanding of data warehousing, data lake architecture, and feature stores


• Ability to design robust ETL pipelines from raw data to model-ready datasets

Research & Collaboration

• Genuine curiosity and a research mindset: comfortable reading papers, prototyping ideas, and iterating quickly


• Contribution to research communities or publications (JMLR, ICLR, NeurIPS, ICML, etc.) is a strong advantage, though not mandatory

Business Acumen

• Track record of translating model outputs into tangible business value: revenue uplift, fraud reduction, customer intelligence


• Comfortable operating in regulated, enterprise environments with legacy system constraints

WHY THIS ROLE

This engagement places you inside one of South Africa's most consequential AI programmes — a rare chance to work on decision-making systems where the stakes are real, the data is rich, and the impact is measurable well beyond a notebook. If you thrive in ambiguity, care about scientific rigor as much as shipping speed, and want to help grow AI talent while building systems that matter, this is the contract for you.