Vacant Roles at IQbusiness

Role Context & Reporting Line:

• Reports to the AI Capability Lead (Data & Analytics).
• Works as part of a multidisciplinary AI delivery team across multiple client business units.
• Engages senior stakeholders, SteerCo and (where appropriate) C-suite, Model Risk and Architecture Boards.
• Supports the build-out of the AI capability: partnerships with Microsoft, AWS, Google, Databricks and Anthropic; pre-sales support; PoC and production delivery on cloud AI solutions.

Key Responsibilities:

AI & Generative AI Engineering

• Design, build and deploy Generative AI and LLM-based applications, including end-to-end RAG agents and agentic / multi-agent solutions.
• Implement RAG pipelines: chunking strategies, embeddings, dynamic indexing, vector databases, vector indexing, grounding and evaluation.
• Build document intelligence solutions: OCR, classification, custom/neural extraction, table extraction and post-processing for unstructured data.
• Implement tool/function calling, prompt engineering, fine-tuning and guardrails for production AI agents.
• Integrate AI models into enterprise systems via APIs, Service Bus, web apps and downstream platforms.
• Experience/knowledge of fine-tuning generative AI models, MCP, AI tool calling, A2A and graph databases.

Cloud AI Solution Delivery Proficient in any of the following (At least 1 CSP) (Azure | AWS | GCP)

• Azure: Azure OpenAI, AI Foundry / Prompt Flow, AI Search, Cognitive Services, Document Intelligence, Functions, Container Apps, Web Apps, Synapse, Data Lake, DevOps CI/CD.
• AWS: Amazon Bedrock (Anthropic/Claude, Titan Embeddings), Lambda, S3 data lakes, Textract and supporting services for AI agents and RAG.
• GCP: Vertex AI, Cloud Run, Google AppSheet and supporting services for AI workloads.
• Microsoft Fabric & Power Platform: Copilot Studio, AI Builder, Power Apps, Power Automate for rapid AI / automation delivery.
• Databricks: notebooks, ML workflows, Lakehouse and Generative AI capabilities.
• Design and implement cloud AI architectures, including migration patterns across hyperscalers where required.

Data Engineering for AI (AI-Data Engineering)

• Design and implement reliable data pipelines (Python, SQL, PySpark) to support ML and AI workloads.
• Prepare, transform and manage structured and unstructured data for AI use cases (ingestion, ETL/ELT, modelling, lakehouse).
• Implement chunking, embedding, indexing and retrieval mechanisms across vector stores.
• Ensure data quality, lineage and governance alignment, including Purview / catalog tooling where applicable.

AIOps & Operationalisation

• Build CI/CD pipelines for ML and AI models (Azure DevOps, GitHub Actions or equivalent).
• Manage model deployment, monitoring, versioning and performance optimisation.
• Implement scalable, secure inference architectures (Container Apps, Lambda, Cloud Run, Functions).
• Apply Responsible AI, model risk, security and compliance practices (RBAC, Key Vault / Secrets Manager, VNets / Private Endpoints, Monitor / Log Analytics).

Consulting & Delivery

• Engage client stakeholders and translate business requirements into AI solution designs.
• Contribute to discovery, design, estimation, costing and commercial models.
• Communicate risks, trade-offs, model assumptions and limitations clearly to technical and business audiences.
• Produce solution architecture, status reports, SteerCo material, governance artefacts and user documentation.
• Support pre-sales, demos, PoCs and RFP responses; contribute to the AI capability roadmap and uplift of junior engineers.

Required Skills & Experience:

• Degree in Computer Science, Data Science, Engineering, Mathematics or a related quantitative field.
• 3+ years' experience delivering AI / ML / data solutions, ideally in a consulting or enterprise delivery environment.
• 1–2+ years' hands-on Generative AI engineering experience (LLMs, RAG, embeddings, vector DBs, prompt engineering).
• 3+ years' broader ML / AI delivery experience (supervised ML, feature engineering, evaluation, NLP).
• Strong data engineering: pipelines, Python / PySpark, data modelling, lakehouse patterns.
• Cloud experience on at least one of Azure, AWS or GCP, with working knowledge of a second; containerisation and CI/CD.
• Experience integrating AI into enterprise systems via APIs, web apps and messaging.
• Business acumen: ability to link AI solutions to business value, ROI and risk.
• Strong communication, stakeholder management, collaboration and analytical skills.

Advantageous Certifications in Any of the following:

Certifications – AWS (AI / ML & Architecture)

• AWS Certified AI Practitioner.
• AWS Certified Machine Learning – Specialty.
• AWS Certified Machine Learning Engineer – Associate.
• AWS Certified Solutions Architect (Associate or Professional).
• AWS Certified Data Engineer – Associate.

Certifications – Microsoft Azure (AI & Data)

• Microsoft Certified: Azure AI Engineer Associate (AI-102).
• Microsoft Certified: Azure AI Fundamentals (AI-900).
• Microsoft Certified: Azure Data Scientist Associate (DP-100).
• Microsoft Certified: Fabric Analytics Engineer Associate (DP-600) or Fabric Data Engineer Associate (DP-700).
• Microsoft Certified: Azure Data Engineer Associate (DP-203).
• Microsoft Certified: Azure Solutions Architect Expert (AZ-305).
• Microsoft Applied Skills credentials in Generative AI, Azure OpenAI, Semantic Kernel, Copilot, AI Builder or Document Intelligence.

Certifications – Google Cloud (AI & Data)

• Google Cloud Certified – Professional Machine Learning Engineer.
• Google Cloud Certified – Generative AI Leader.
• Google Cloud Certified – Professional Data Engineer.
• Google Cloud Certified – Professional Cloud Architect.
• Google Cloud Certified – Cloud Digital Leader.

Certifications – Other AI / Data Platforms

• Databricks Certified Generative AI Engineer Associate.
• Databricks Certified Machine Learning Associate / Professional.
• Databricks Lakehouse Fundamentals / Data Engineer.
• Anthropic / Claude developer credentials.
• NVIDIA Deep Learning Institute (DLI) certifications in Generative AI or LLMs.
• Harvard or other recognised Data Science / Machine Learning credentials.

Other Advantageous Experience

• Microsoft Fabric, Azure AI Foundry, Azure OpenAI and solution delivery experience.
• AWS Bedrock with Anthropic Claude, Titan Embeddings and Textract in production.
• GCP Vertex AI and Cloud Run delivery experience.
• Knowledge graphs, advanced RAG patterns, agent orchestration and multi-agent frameworks.
• Exposure to Model Risk Management (MRM), Architecture Review Boards and Responsible AI frameworks.
• Experience productising AI solutions and contributing to AI CoE / Target Operating Model design.
• Track record in pre-sales, RFPs, technical demos and client workshops.

Success Measures:

• Production-grade AI solutions deployed across Azure, AWS and / or GCP.
• Scalable, governed data and AI pipelines established and reused across engagements.
• Measurable contribution to revenue, pre-sales and RFP wins.
• Reduced time-to-production for new AI use cases through reusable patterns and accelerators.
• Demonstrable mentorship of junior engineers and uplift of the broader AI capability.
• High-quality stakeholder engagement, SteerCo and executive communication.

Closing Date 30 September 2026

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About the Role

• We are seeking a Senior Mobile Engineer with a strong focus on designing, developing, and maintaining high-performance mobile applications using Kotlin Multiplatform.
• This role requires expertise in native Android and iOS development, and you will leverage Kotlin/Jetpack Compose and SwiftUI to deliver seamless cross-platform solutions.

Key Responsibilities

• Design and implement high-quality mobile applications across Android and iOS platforms using Kotlin Multiplatform.
• Collaborate with cross-functional teams to define, design, and ship new features.
• Maintain and improve existing mobile applications by troubleshooting and resolving issues.
• Optimize application performance for both Android and iOS devices, ensuring a seamless user experience.
• Stay up to date with emerging trends and technologies in mobile application development.
• Write clean, maintainable, and efficient code while adhering to best practices and coding standards.
• Conduct code reviews and provide constructive feedback to team members.
• Mentor junior engineers and contribute to their professional development.

Requirements

• Demonstrable experience in native mobile development for both Android and iOS platforms.
• Strong proficiency in Kotlin and Kotlin Multiplatform, including Jetpack Compose.
• Proficient in native iOS development with Swift and SwiftUI.
• Solid understanding of mobile application architectures and design patterns.
• Proven track record of delivering high-performance mobile applications.
• Familiarity with RESTful APIs and third-party libraries integration.
• Excellent problem-solving skills and attention to detail.

Preferred Qualifications

• Experience with CI/CD tools and practices for mobile application development.
• Knowledge of mobile application security best practices.
• Familiarity with Agile development methodologies.
• Experience with Unit and UI testing frameworks for mobile applications.

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Role Summary

• iqbusiness is seeking experienced Senior Business Analysts for contract opportunities within the financial services sector, with a strong focus on Trade Finance.
• The role requires a strong understanding of Trade Finance processes, products, and systems, with the ability to identify control gaps, improve operational efficiency, and enable compliant, scalable solutions across the trade lifecycle.

Key Responsibilities

• Translate trade finance business, regulatory and compliance requirements into clear business, functional, and non-functional requirements
• Analyse end-to-end trade lifecycles (origination, documentation, processing, settlement) and identify risks, control gaps, and operational inefficiencies
• Define, document and optimise current- and future-state Trade processes, ensuring full coverage across front, middle and back-office functions
• Produce high-quality analysis artefacts including BRS, FSDs, user stories, use cases, data models, SOPs, and process maps aligned to Trade Finance standards
• Participate in Trade system implementations, upgrades, and integrations, including SWIFT messaging and document management platforms
• Collaborate with Solution Architects and Trade system vendors (e.g. Finastra, Eximbills, Surecomp) to translate business requirements into technical solutions
• Support UAT planning and execution, including test case design, defect management, and traceability to trade requirements and business scenarios

Requirements

• Minimum 6–10 years’ experience as a Business Analyst in complex, multifaceted financial services environments
• Hands‑on experience across Agile, Waterfall, or hybrid delivery models
• Strong business analysis fundamentals aligned to BABOK and SDLC
• Strong stakeholder facilitation, influencing, and negotiation skills
• Excellent written and verbal communication skills
• Strong analytical skills, attention to detail, and conceptual thinking
• Experience with Jira, Confluence, SharePoint, and SQL for requirements and analysis

Education

• Matric (required)
• Relevant tertiary qualification (Bachelor’s Degree, Diploma, or recognised certification from an accredited institution)

Closing Date 03 July 2026

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Job Description

• We are recruiting an AI Solutions Architect to lead the design and delivery of enterprise-grade AI and Generative AI solutions across cloud platforms, with a strong emphasis on production deployment, business value and consulting-led delivery.

This role sits at the intersection of:

• Solution architecture (end-to-end systems design)
• AI engineering (capability awareness, not hands-on build ownership)
• Consulting (client engagement, commercial alignment, pre-sales)

The successful candidate will translate complex business problems into scalable AI architectures, lead multidisciplinary teams, and ensure AI solutions are aligned to enterprise systems, governance, and measurable outcomes.

Role Context & Positioning:

• Senior member of the AI & Data capability working across multiple client engagements
• Acts as the bridge between AI engineering, architecture, and business stakeholders
• Owns solution design, architecture governance and delivery oversight
• Plays a key role in pre-sales, client shaping, and capability development

Responsibilities:

AI Solution Architecture & Design

Lead the design of end-to-end AI architectures across data, application and integration layers

• Design solutions spanning:
• Generative AI (LLMs, RAG, agents)
• Document intelligence and automation
• Enterprise AI platforms and APIs

Define:

• Data flow, integration patterns, and system architecture
• Retrieval, orchestration and agent interaction patterns
• Security, governance and deployment architectures

Client Advisory & Solution Shaping

• Lead discovery workshops and use case definition sessions
• Translate business problems into AI-enabled solutions and architecture blueprints

Advise clients on:

• AI adoption roadmaps
• Architecture approaches (build vs buy vs hybrid)
• Trade-offs, risks, and ROI

Delivery Leadership

Own architecture across delivery lifecycle:

• Discovery → design → build oversight → deployment → optimisation

Guide engineering teams on:

• Architecture decisions
• Design patterns and best practice

Ensure:

• Production-grade delivery
• Alignment to enterprise systems and constraints

Cloud AI Architecture

Architect solutions across at least one hyperscaler (Azure preferred), including:

• Azure OpenAI, AI Foundry, AI Search, Document Intelligence
• Equivalent AWS (Bedrock) or GCP (Vertex AI) services

Define:

• Deployment patterns (APIs, microservices, serverless)
• Integration into enterprise ecosystems
• Security, networking and governance models

Data & AI Platform Design

Design data foundations required for AI:

• Data pipelines, ingestion patterns, storage and modelling
• Vector databases, embeddings and retrieval strategies

Ensure:

• Data quality, lineage, and governance alignment
• AI-readiness of enterprise data platforms

Pre-Sales & Commercial Contribution

Support and lead:

• Solution design for proposals and RFPs
• Estimation, costing and effort modelling

Contribute to:

• Client pitches and demos
• Opportunity shaping and deal conversion

Capability Building & Thought Leadership

Develop:

• Reference architectures and reusable solution patterns

Mentor:

• Engineers and consultants

Contribute to:

• Internal capability development and AI maturity

Requirements

Consulting & Leadership

• 7–12+ years in technology, data or solution architecture
• 3–5+ years in consulting / client-facing architecture roles

Proven experience:

• Leading AI or data engagements
• Managing multidisciplinary teams
• Engaging senior stakeholders and executives

AI & Generative AI

Practical experience designing solutions involving:

• LLMs and Generative AI applications
• RAG architectures and retrieval systems
• AI agents / orchestration patterns

Strong understanding of:

• Prompting, evaluation and guardrails
• Enterprise AI use cases and limitations

Solution Architecture

Strong experience designing:

• Distributed systems and microservice architectures
• API-driven integrations
• Enterprise-scale cloud solutions
• Ability to clearly articulate architecture decisions and trade-offs

Cloud (At least one CSP, Azure preferred)

• Azure (preferred): OpenAI, AI Foundry, Synapse, Data Lake, App Services
• AWS: Bedrock, Lambda, S3
• GCP: Vertex AI, Cloud Run

Data & AI Platform Understanding

Strong grounding in:

• Data engineering concepts (pipelines, modelling, lakehouse)
• AI system data flows (embeddings, chunking, indexing)
• Experience designing AI-ready data ecosystems

Business & Communication

Ability to:

• Translate technical designs into business outcomes
• Communicate with C-suite and architecture boards
• Strong commercial acumen and delivery mindset

Closing Date 30 September 2026