• Lead AI modelling projects end-to-end, from problem definition through to a validated solution that a partner team can understand, adopt, and maintain. • Advise product, data, and engineering teams on AI solution design, including model selection, data requirements, architecture decisions, and the tradeoffs involved in each. • Serve as a technical reference across the C4E and its partner teams: review approaches, answer hard questions, and provide a grounded second opinion on high-stakes decisions. • Adapt model designs and methods based on partner feedback and validation results, balancing technical rigour with practical constraints. • Identify where the team's modelling practices could be stronger and act on it: better evaluation approaches, shared templates, clearer processes. • Create reusable technical resources such as design patterns, evaluation frameworks, and model cards, and actively facilitate knowledge sharing across disciplines. • Collaborate with the Responsible AI, Data, Platform, and Security teams, ensuring the right people are involved at the right stage and feeding recurring patterns or gaps back to them. • Support partner adoption by producing documentation and handover materials that are genuinely usable, and staying involved until teams are confident with what has been built.

• Design and deploy AI-powered workflows and automation across internal systems • Integrate LLM APIs (OpenAI, Anthropic, etc.) into operational platforms and internal tooling, with examples including: - Airtable Scripting & AI Automation - Microsoft 365 / Power Platform AI Enhancements - Custom Agent & RAG Pipeline Development • Build and maintain custom AI agents, automation pipelines, and retrieval-augmented generation (RAG) systems • Own the bridge between AI capabilities and the existing technology stack • Evaluate and prototype emerging AI tools for business relevance and operational fit • Collaborate with operations, sales, and leadership teams to identify and prioritize AI opportunities • Contribute to platform development features and maintain clean, well-documented code • Translate AI capabilities for non-technical stakeholders and gather real requirements from business teams • Maintain a running roadmap of AI enhancements tied to business goals and project timelines • Consistently exemplify established best practices, serving as the internal subject matter expert on applied AI • Provide team or company-wide AI training and education where applicable. • Create & manage technical documentation for all AI systems, integrations, and workflows.

• Lead the technical architecture of Data, Analytics and AI solutions for our clients, covering the full lifecycle from design to deployment: • Design end-to-end data architectures: data lakes, lakehouses, warehouses, and streaming pipelines. • Define standards for data modeling, storage, ingestion, and transformation across client engagements. • Architect MLOps and AI deployment infrastructure (model registries, CI/CD for ML, monitoring). • Lead technical decisions on cloud platforms (Azure, AWS, GCP) and open-source tooling. • Define best practices and reusable frameworks for data engineers, analysts, and data scientists. • Act as a technical mentor and reviewer for cross-functional project teams. • Bridge the gap between data analysts, data engineers, and AI/ML engineers on complex projects. • Contribute to internal knowledge base, toolkits, and delivery accelerators. • Lead architecture workshops and discovery sessions with client stakeholders. • Translate business requirements into scalable, robust technical blueprints. • Present architecture decisions to both technical teams and executive audiences. • Support pre-sales and proposal efforts with technical scoping and solution design. • Provide internal training and knowledge-sharing sessions with the team. • Support the Head of Practice on business development and internal capability initiatives.

• As a Principal ML Architect, you will be the senior technical leader for our most ambitious client projects. You will act as a trusted advisor to clients, a mentor to your engineering team, and the chief architect for complex AI systems. This is a hands-on leadership role that combines deep technical expertise with strategic client management. • Lead Technical Strategy & Architecture: Partner with clients to understand their core business challenges and design sophisticated, scalable solutions leveraging machine learning, search, and agentic AI paradigms. • Manage Client Engagements: Serve as the primary technical point of contact for client stakeholders, translating complex concepts, managing expectations, and ensuring project success from a technical and business perspective. • Lead & Mentor Engineering Teams: Guide a team of talented ML engineers, fostering a culture of excellence, providing technical direction, and taking ultimate responsibility for project delivery. • Drive Innovation: Move beyond conventional ML to architect and implement cutting-edge agentic workflows, agent-based modeling, multi-agent systems, and complex RAG pipelines that deliver transformative value. • Oversee the Full Project Lifecycle: Steer projects from initial discovery and architectural design through to deployment, monitoring, and iteration, ensuring solutions are robust, efficient, and production-ready.