Senior AI Engineer

Betway Africa; Johannesburg; Hybrid; ZAR 900,000 - 1,300,000; Full time

Benefits offered by this job

Hybrid work schedule

Job summary

Betway Africa is seeking an AI/ML Engineer to translate business problems into scalable AI solutions. You will design models, build RAG systems, and deploy AI services on Azure, collaborating with data and software teams to ensure robust delivery.

The role emphasizes responsible AI practices, governance, and continuous learning, with a hybrid work arrangement and opportunities to impact online gaming platforms.

Qualifications

• Degree in CS or related quantitative field.
• Hands-on AI/ML production experience.
• Azure AI certifications preferred.
• Strong Python and SQL skills.
• Experience with RAG, NLP and speech-to-text is a plus.

Responsibilities

• Translate business problems into AI/ML solutions with clear success criteria.
• Design and build generative AI and RAG solutions.
• Develop agentic AI workflows with tool calls.
• Ensure model evaluation and performance monitoring.
• Collaborate with data engineering and software teams.

Skills

AI/ML engineering

Python

SQL

Azure ecosystem

Model evaluation

Education

Degree in Computer Science or related field

Azure AI certification (AI-102 / DP-100)

Tools

Azure Machine Learning

Azure OpenAI Service

Azure AI Foundry

LangChain

GitHub Actions

Job description

Kick-start your career in the online gaming world and experience the very latest in technology and innovation. You'll take ownership of work that gives us our competitive edge, including:

Solution design & machine learning modelling

• Translate business problems into well-scoped AI and machine learning solutions with clear success criteria.
• Build, train, evaluate, and tune machine learning models (classification, regression, clustering, forecasting, recommendation, anomaly detection).
• Perform feature engineering, data preparation, and exploratory analysis on structured and unstructured data.
• Select the right approach for the problem, including knowing when a simpler or non-AI solution is the better answer.

Generative AI, LLMs & RAG

• Design and build retrieval-augmented generation (RAG) solutions, including chunking strategies, embeddings, vector and hybrid search, re-ranking, and grounding.
• Integrate and orchestrate large language models through Azure OpenAI and Azure AI Foundry.
• Apply prompt engineering, structured outputs, function and tool calling, and where appropriate fine-tuning or distillation.
• Evaluate generative solutions for accuracy, latency, and cost, and manage token and inference spend.

Agentic AI & automation

• Build agentic AI solutions that plan, call tools and APIs, and complete multi-step tasks with appropriate guardrails and human-in-the-loop checkpoints.
• Implement multi-agent orchestration patterns using frameworks such as Semantic Kernel, Azure AI Agent Service, AutoGen, or LangGraph.
• Define agent tooling, memory, state management, and failure and escalation handling.

NLP & speech

• Deliver natural language processing capabilities such as entity extraction, classification, summarisation, sentiment analysis, and intent recognition.
• Implement speech-to-text and related speech capabilities, including batch and real-time transcription and custom or domain-specific models.
• Apply document intelligence and OCR techniques to extract structure from documents and forms.

MLOps, deployment & operations

• Deploy models and AI services as APIs, containers, or managed endpoints, with automated CI/CD pipelines.
• Implement experiment tracking, model registries, versioning, and reproducible training pipelines.
• Monitor production solutions for performance, drift, quality, cost, and reliability, and act on what the monitoring shows.
• Troubleshoot and resolve production issues and support solutions in use by the business.

Responsible AI, security & governance

• Apply responsible AI practice: bias and fairness checks, content safety, evaluation harnesses, and clear limitations.
• Handle data in line with group data protection, privacy, and regulatory requirements, including PII handling and data residency.
• Secure solutions using managed identities, key management, network controls, and least-privilege access.
• Maintain auditability of models, prompts, data sources, and decisions.

Collaboration & documentation

• Work with data engineering on pipelines, feature availability, and data quality.
• Partner with software engineering to integrate AI capability into products and internal platforms.
• Explain approaches, trade-offs, and results to technical and non-technical audiences.
• Maintain solution documentation, architecture notes, evaluation results, and runbooks.

Technical environment (current technologies) Cloud platform

• Microsoft Azure as the primary cloud platform.
• Azure Machine Learning, Azure AI Foundry, and Azure OpenAI Service.
• Azure AI Search for vector, keyword, and hybrid retrieval.
• Azure AI Services: Speech, Language, Document Intelligence, Vision, and Content Safety.
• Azure Functions, Azure Container Apps, and AKS for hosting and inference.
• Azure Key Vault, Microsoft Entra ID, managed identities, and Azure Monitor / Application Insights.

Data & storage

• Azure Data Lake Storage, Azure SQL, and Cosmos DB (including vector store usage).
• Azure Databricks, Microsoft Fabric.
• Strong SQL and working with both structured and unstructured data at volume.

Languages, frameworks & tooling

• Python as the primary language, with pandas, scikit-learn, PyTorch or TensorFlow, and Hugging Face Transformers.
• LangChain, LlamaIndex, Semantic Kernel, and comparable orchestration frameworks.
• MLflow and Prompt flow for experiment tracking, evaluation, and lifecycle management.
• Git, Azure DevOps, GitHub Actions, Docker, and infrastructure-as-code (Bicep or Terraform).
• REST API development, typically with FastAPI or equivalent.

Technical requirements & qualifications

• 3-5 years of hands-on experience in AI, machine learning, or data science engineering, including solutions running in production.
• Degree in Computer Science preferred; Engineering, Information Systems, Data Science, Statistics, or a related quantitative field also considered.
• Professional certification preferred, such as Microsoft Certified: Azure AI Engineer Associate (AI-102) or Azure Data Scientist Associate (DP-100).
• Demonstrable experience building on Azure AI and machine learning services.
• Proven delivery across several of: ML modelling, RAG, LLM applications, agentic AI, NLP, and speech-to-text.
• Strong Python and SQL, with clean, tested, maintainable code.
• Sound understanding of model evaluation, and the ability to prove a solution actually works before it ships.
• Effective troubleshooting of data, model, and integration issues.
• Able to work effectively independently and within a cross-functional team.
• Commitment to continuous learning in a field that moves quickly.

Advantageous experience

• Software engineering background: production application development, API design, testing, code review, and CI/CD.
• Containerisation and microservice or event-driven architecture.
• Additional certifications such as Azure Data Engineer Associate (DP-203), Azure Developer Associate (AZ-204), or Azure Solutions Architect Expert (AZ-305).
• .NET / C# alongside Python.
• Experience in online gaming, betting, payments, or another regulated, high-volume digital environment.
• Exposure to real-time or streaming data, and to fraud, risk, personalisation, or responsible-gaming use cases.
• Front-end or rapid prototyping skills for demonstrating solutions (Streamlit, Gradio, React).

Working arrangement

• Hybrid: Monday to Thursday in office, Friday from home.
• Occasional flexibility may be required to support production releases or incident resolution.