Senior AI Engineer

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:

• 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, Auto Gen, or Lang Graph.
• 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.

• 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 , managed identities, and Azure Monitor / Application Insights.

• 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.

• Python as the primary language, with pandas, scikit-learn, PyTorch or Tensor Flow, and Hugging Face Transformers.
• Lang Chain, Llama Index, Semantic Kernel, and comparable orchestration frameworks.
• MLflow and Prompt flow for experiment tracking, evaluation, and lifecycle management.
• Git, Azure Dev Ops, Git Hub Actions, Docker, and infrastructure-as-code (Bicep or Terraform).
• REST API development, typically with FastAPI or equivalent.

• 3-5 years of hands-on experience in…