Senior AI Engineer
Greentube is hiring a Senior AI Engineer to join their team. This role focuses on designing, building, and deploying advanced AI and machine learning solutions. The engineer will translate business problems into well-scoped AI and machine learning solutions with clear success criteria.
The position involves building, training, evaluating, and tuning machine learning models, as well as performing feature engineering, data preparation, and exploratory analysis. Generative AI, LLMs, and RAG solutions will be designed and built, integrating and orchestrating large language models through Azure OpenAI and Azure AI Foundry. Agentic AI and automation will be developed to plan, call tools and APIs, and complete multi-step tasks.
Natural language processing capabilities such as entity extraction, summarisation, sentiment analysis, and intent recognition will be delivered. Speech-to-text and related speech capabilities will be implemented. MLOps, deployment, and operations will be a key focus, including deploying models and AI services as APIs, containers, or managed endpoints with automated CI/CD pipelines. Production solutions will be monitored for performance, drift, quality, cost, and reliability, and troubleshooting of production issues will be undertaken.
Responsible AI, security, and governance practices will be applied, including bias and fairness checks, content safety, and clear limitations. Data handling will be in line with group data protection, privacy, and regulatory requirements. Solutions will be secured using managed identities, key management, network controls, and least-privilege access. Auditability of models, prompts, data sources, and decisions will be maintained.
Collaboration with data engineering on pipelines, feature availability, and data quality is expected. Partnership with software engineering to integrate AI capability into products and internal platforms will be required. Approaches, trade-offs, and results will be explained to technical and non-technical audiences. Solution documentation, architecture notes, evaluation results, and runbooks will be maintained.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 3-5 years of hands-on experience in AI, machine learning, or data science engineering, including solutions running in production (required).
- Degree in Computer Science preferred; Engineering, Information Systems, Data Science, Statistics, or a related quantitative field also considered (preferred).
- Professional certification preferred, such as Microsoft Certified: Azure AI Engineer Associate (AI-102) or Azure Data Scientist Associate (DP-100) (preferred).
- Demonstrable experience building on Azure AI and machine learning services (required).
- Proven delivery across several of: ML modelling, RAG, LLM applications, agentic AI, NLP, and speech-to-text (required).
- Strong Python and SQL, with clean, tested, maintainable code (required).
- Sound understanding of model evaluation, and the ability to prove a solution actually works before it ships (required).
- Effective troubleshooting of data, model, and integration issues (required).
- Able to work effectively independently and within a cross-functional team (required).
- Commitment to continuous learning in a field that moves quickly (required).
- Software engineering background: production application development, API design, testing, code review, and CI/CD (nice-to-have).
- Containerisation and microservice or event-driven architecture (nice-to-have).
- Additional certifications such as Azure Data Engineer Associate (DP-203), Azure Developer Associate (AZ-204), or Azure Solutions Architect Expert (AZ-305) (nice-to-have).
- .NET / C# alongside Python (nice-to-have).
- Experience in online gaming, betting, payments, or another regulated, high-volume digital environment (nice-to-have).
- Exposure to real-time or streaming data, and to fraud, risk, personalisation, or responsible-gaming use cases (nice-to-have).
- Front-end or rapid prototyping skills for demonstrating solutions (Streamlit, Gradio, React) (nice-to-have).
Betway Africa is the African arm of Betway, the sports-betting and online-casino brand owned by Super Group (SGHC), a holding company listed on the New York Stock Exchange. Operating since 2006, it delivers sports betting and casino products - including the Jackpot City, Lucky Nugget and Ruby Fortune brands - across markets such as South Africa, Ghana, Nigeria, Zambia, Tanzania, Malawi and Mozambique. The division employs more than 1,000 people, with offices in Johannesburg and Cape Town. It covers functions spanning marketing, technology, finance and customer care for Betway's African operations.