AI Engineer Consultant

Role: AI Engineer Consultant Function: Digital Strategy, Architecture & Innovation Engagement: Initial 3–4 months Start: As soon as possible following selection and mobilisation Resource Level: Senior hands-on technical consultant Role Purpose We are seeking a strong, hands-on AI Engineer Consultant to provide practical AI engineering and applied data science capability across a number of active AI initiatives. The consultant will work closely with a Data Scientist and collaborate with architecture, IT, data,business and delivery stakeholders. The focus is on advancing existing prototypes and technical concepts into maintainable, documented and reusable solutions, rather than starting greenfield research initiatives. Key Areas of Responsibility 1. AI Engineering & Solution Development • Advance existing AI prototypes and engineering pipelines. • Develop maintainable AI/ML components using existing Microsoft Azure services. • Improve web-search orchestration, evidence capture, provenance, quality controls and assessment workflows. • Implement persistent storage, versioning and incremental processing. • Develop or prototype consumption interfaces such as dashboards, M365 agents or lightweight web applications. • Package solutions using sound software-engineering, security and deployment practices. Request_for_AI_Engineer_Consult… 2. AI Architecture & Technical Documentation • Develop technical and architecture documentation for AI solutions and enterprise integration patterns. • Document architecture, interfaces, data flows, security considerations, assumptions, risks, deployment requirements and operational responsibilities. • Develop reusable solution blueprints, templates, diagrams, checklists and handover documentation. • Support enterprise integrations, including systems such as Salesforce and ServiceNow. Request_for_AI_Engineer_Consult… 3. AI/ML Classification & Experimentation • Develop and evaluate text-classification approaches. • Compare LLM, non-LLM, hybrid and minimal-LLM approaches. • Evaluate models based on accuracy, cost, latency, explainability, robustness and maintainability. • Design reproducible experiments and document recommendations. • Assess emerging classification and decision models available through Microsoft Foundry. 4. Enterprise AI Integration • Build or integrate AI solutions through APIs. • Work with structured and unstructured data sources and knowledge repositories. • Support grounded search, RAG, evidence-management and agentic workflows. • Contribute to enterprise AI solution design, integration and operationalisation. 5. Engineering Governance & Knowledge Transfer • Participate in code reviews, technical design reviews and demonstrations. • Maintain source control, work tracking and technical decision records. • Produce reusable code, configuration/deployment guidance and documentation. • Conduct knowledge transfer and prepare handover materials. Requirements Education & Professional Profile • Degree in Computer Science, Data Science, Artificial Intelligence, Engineering or related discipline, or equivalent relevant professional experience. • Strong analytical and problem-solving capability. • Excellent written and spoken English. • Ability to communicate technical concepts effectively to both technical and non-technical stakeholders Candidates should demonstrate strong hands-on experience in: • Python • AI / Machine Learning / Generative AI solution development • Software engineering practices including modular development, testing and code review • Git/source control and CI/CD • Microsoft Azure • Azure AI Services / Microsoft Foundry • Azure Databricks • Azure SQL Database / SQL Server • Azure DevOps • Text classification and machine-learning experimentation • Model evaluation and cost/performance optimisation • API-based enterprise integrations • Structured and unstructured data • Technical architecture and solution documentation • Security, access control, data protection and responsible AI considerations. Highly Desirable Experience Preference will be given to candidates with experience in: • Microsoft Copilot Studio • Microsoft Power Platform • RAG and grounded web search • Evidence provenance • Knowledge engineering • Agentic workflows and model orchestration • Comparing LLMs, smaller models, traditional ML, rules-based and hybrid approaches • Global health, international development, public sector or similarly regulated environments • Relevant Microsoft certifications in Azure AI, Data, Cloud or Power Platform.