Senior AI EngineerTeam: AI Squad | ITReporting to: Group Manager – Data & AIThe opportunity: We're looking for a Senior AI Engineer to design, build, and operate production-grade AI systems that deliver measurable business value. This role sits at the intersection of engineering excellence, applied AI, and real-world outcomes — not experiments for experiment's sake.You'll work on modern AI solutions including LLMs, RAG, agents, and ML services, deployed at scale on Azure and Databricks, with strong governance, security, and operational discipline.What you'll be doingBuild and deploy AI services and APIs for inference and retrieval (LLMs, RAG, ML models, agents)Develop data pipelines for training and retrieval with strong governance, privacy, and auditabilityContribute to the AI platform and MLOps toolchain (CI/CD for models, MLflow, feature stores, monitoring, rollback)Translate business and product requirements into well-scoped technical deliverablesWrite clean, well-tested code and contribute reusable components and patternsOperate AI workloads in production with clear SLIs/SLOs (availability, latency, cost)Apply security-by-design practices including prompt protection, content filtering, access controlsMaintain documentation, runbooks, and architectural decision recordsYou'll have this experience: Leading the end-to-end delivery of production AI solutions (e.g., RAG assistants, classification/ranking, NLP models) aligned with business goals.Overseeing and optimising AI operations on enterprise platforms (App Services, Containers, Databricks) for scalability and reliability.Driving best practices and high-impact outcomes in cross-functional teams, collaborating with product, data, and talent stakeholders.You'll have these skillsAdvanced engineering in Python and microservices, with expertise in cloud-native architecture on Azure.Proficient with Databricks (Delta, Unity Catalog, MLflow), Azure OpenAI/Azure ML, and modern retrieval patterns (RAG, tools/agents, function calling).Strong data engineering foundation: batch and streaming pipelines, data quality, governance, and lineage.Applied practical security for AI systems, including prompt protection, privacy safeguards, content filtering, and auditability.Ability to translate real customer problems and feedback into technical solutions—ensuring that every AI system, service, or feature directly addresses customer pain points and delivers measurable value.Continuously seeking improvement by staying curious, embracing challenges, and learning from mistakes in delivering better solutionsPreferred qualificationsRelevant tertiary qualifications.Hands‐on with Databricks/MLflow, Azure OpenAI, Azure DevOps, Azure services (Functions, Key Vault, App Insights).Relevant certifications (e.g., Microsoft Azure, Databricks) or equivalent practical experience.Why this roleWork on real, production AI in an enterprise environment — not demosStrong focus on engineering quality, security, and operational excellenceInfluence platform standards and reusable AI patternsClear connection between your work and business impactCurious to know more? Apply today for a confidential discussion. We look forward to connecting with you