AI Engineer – Agentic AI Applications Gauteng

Within ICIB we believe in deeply embedding our engineers into the different businesses that we run. As an AI engineer, you will sit close to the business teams you support, building an understanding of the workflows, risks and opportunities where applied AI can deliver measurable value. Our technical skillset includes AI engineering, software delivery, data science, business analytics, automation, workflow design, compliance and risk-aware solution development. We use enterprise data assets, business knowledge, document repositories, APIs, workflow tools and market information to build practical AI applications that improve decision-making, productivity, control and client outcomes. The expectation of the role is to design, prototype, evaluate and support the development and deployment of AI applications, including LLM-powered agents, RAG assistants, workflow automations and controlled pilots that can progress toward production use. Our model is that of project ownership, asking our consultants to build end-to-end partnerships and solutions as per business requirements. From engaging stakeholders on the business need, to developing and evaluating technical solutions, to presenting findings through clear storytelling; you will own and drive projects. The candidate's technical responsibility will focus on building, testing, reviewing and delivering safe, reusable AI solutions, quantifying business value and driving our AI capability forward. A risk-conscious approach to toolset, model, prompt, data source and workflow selection is critical, with a firm view to ensure auditability, security, fairness and appropriate human oversight. Adherence to important regulatory standards like POPIA is a must. Your role will be project based with day-to-day development of solutions, engagements with key stakeholders, prioritization of AI opportunities and the ability to create a platform for generating, testing and scaling new ideas.

Provide specialized AI engineering expertise, with a clear understanding of prioritization to deliver commercial value aligned with strategic objectives In collaboration with others, translate business workflows into focused, buildable AI use cases and delivery plans Design, build and test agentic AI applications using Python, Azure services and Microsoft Foundry Develop reusable components for prompt orchestration, RAG, tool/function calling, state handling, API integration and output formatting Integrate AI applications with internal APIs, databases, document stores, workflow tools, search/vector, graph databases, indexes and enterprise systems Implement evaluation datasets, test cases, scoring metrics and review loops for LLM and agent outputs Instrument AI applications with traces, logs, dashboards, latency/cost metrics, output capture and error monitoring Support controlled deployment into sandbox, pilot and production-adjacent environments using Azure-aligned patterns Engage with key stakeholders to improve, deliver, pivot and review strategic AI initiatives Document solution designs, assumptions, evaluation results, risks, limitations, controls and handover requirements Collaborate with technology, data, risk, compliance and governance teams when moving prototypes toward production Maintain security and audit controls in order to protect AI solutions, data sources and their environment Keep abreast of latest developments in AI, data, technology, banking and world events

Qualifications, Experience and Skills

Tertiary qualification in Computer Science, Engineering, Data Science, Mathematics, or a related technical field, or equivalent practical experience. Demonstrated experience in problem-solving and delivering meaningful contributions to software engineering, data, artificial intelligence (AI), or machine learning (ML) projects. 3–5 years' relevant experience within a software engineering, data engineering, AI, or ML environment, or equivalent demonstrable project experience. Strong Python development skills, with the ability to design, build and maintain services, APIs, automation scripts and AI-powered solutions using clean, scalable and well-tested code. Working knowledge of Azure services, Microsoft Foundry, Git version control, automated testing practices, CI/CD principles, containerisation technologies (Docker) and AI-assisted coding tools. Sound understanding of Large Language Models (LLMs), prompt engineering, tool and function calling, Retrieval-Augmented Generation (RAG), embeddings, memory and context management, agent-based workflows, and common AI application failure modes. Advantageous experience with Azure OpenAI, Azure AI Search, LangChain, LangGraph, Semantic Kernel, vector databases, SQL, Pandas, NumPy, Databricks and/or Microsoft Fabric. Ability to develop practical AI-enabled applications and reusable engineering frameworks and patterns. Strong commercial and business acumen, with the ability to understand and deliver value-driven technology solutions that support strategic objectives. Proven ability to analyse, interpret and solve complex technical and business challenges. Ability to work independently, manage defined workstreams and deliver outcomes with minimal supervision. Demonstrated ability to collaborate effectively and integrate seamlessly into an established team environment. Excellent verbal and written communication skills, with the ability to engage both technical and non-technical stakeholders. Strong analytical, critical thinking and decision-making capabilities. Naturally inquisitive mindset with a passion for continuous learning, innovation and improvement. Ability to contribute to, influence and support strategic initiatives and organisational objectives. Comfortable working in an iterative delivery environment that embraces experimentation, continuous improvement and user feedback. Results-oriented with a strong focus on achieving quality outcomes and meeting commitments. Consistently delivers work to a high professional standard. Ability to manage multiple priorities and perform effectively under pressure in a fast-paced environment. Strong attention to detail, with a focus on quality, security, governance and auditability. Self-motivated and proactive, demonstrating initiative and ownership while requiring minimal direction. Proven ability to operate effectively within a fast-paced, highly technical and cross-functional environment.