Senior Manager, AI Architect

Job description

### About Baringa Baringa is a global consulting firm that partners with leaders to drive change and create value. With deep industry expertise, and enabled by advanced technology, the firm helps clients to deliver with greater confidence and certainty. With over 2,000 people across the UK, Europe, North America, Asia and Australia, the firm combines global insight with local understanding. The firm works across energy and resources, financial services, government and public sector, consumer products and retail, pharmaceuticals and life sciences, manufacturing, and technology, media and telecoms, with capabilities spanning strategy, transformation and operational excellence – all powered by advanced technology, data, AI and digital innovation. Clients value Baringa’s collaborative approach and the way its teams integrate seamlessly – all working with a shared understanding of what matters most. The firm is known for its kind, curious experts who listen closely and care deeply about client success as they help clients transform energy markets, modernise financial platforms, expand telecoms and digital networks through advanced data analytics, enable digital services in government, and unlock growth in consumer sectors. Certified as a *Great Place to Work* around the world, Baringa has been recognised by the *Financial Times* in 22 categories of its UK Leading Management Consultants rankings, and by *Forbes* for four consecutive years as one of the World’s Best Management Consulting Firms. ## Our Solutions & AI Lab (SAIL) practice are looking for an experienced AI Architect to join the team. Our Solutions & AI Labs practice helps clients control their data, turn it into actionable insight, and better leverage it through AI and Machine Learning solutions embedded directly into business processes. We support clients across a range of industries and offer deep expertise in AI/ML, Cloud, Platform Engineering and Managed Solutions. We are looking for an experienced Senior Manager to serve as an AI Architect — someone who can design the end-to-end architecture for enterprise AI platforms, lead consultancy engagements, and grow our AI & Solutions Engineering capability. The AI Architect role owns the technical blueprint for AI-enabled solutions delivered in and around our clients’ environments, combining deep architectural specialism with the commercial and leadership skills to shape and grow our practice. This is a role for someone who thrives at the intersection of client advisory, AI platform architecture and hands-on delivery leadership — and who has a genuine passion for bringing AI systems to production at scale. ### What you will be doing As a Senior Manager and AI Architect, you will own and lead the architecture of complex AI programmes end-to-end — from shaping the opportunity and winning the work, through solution design, delivery governance and team leadership. You will bring SME-level depth in enterprise AI architecture and apply it to create real, lasting value for our clients. Although we do not expect you to be an expert in every technology listed below simultaneously, our team consists of people who can advise clients as well as bring deep technical knowledge when needed. Key responsibilities span the following areas. **Engagement & delivery leadership** - Own and lead the architecture of complex AI and data transformation engagements, taking accountability for solution design, quality, technical risk and client outcomes. - Lead and resource multi-disciplinary delivery teams — architects, engineers, data scientists and consultants — providing clear direction, technical oversight and people development. - Manage engagement resourcing: forecast team requirements, work with practice leadership to staff engagements, and develop the talent pipeline through mentoring of junior practitioners. - Proactively identify and manage delivery and architectural risks in complex stakeholder environments, escalating appropriately and maintaining client confidence throughout. - Communicate clearly to both technical teams and senior client stakeholders, translating architectural complexity into actionable insight and decisive recommendation. - Conduct rigorous architecture and design reviews and uphold engineering standards across every engagement you lead. **Stakeholder management & advisory** - Act as a trusted advisor to client architects, CTOs, CDAOs and engineering leads, earning trust through depth of knowledge and a demonstrable delivery track record. - Build and maintain strong, senior client relationships, navigating competing priorities across business, technology and risk functions. - Facilitate architecture workshops and decision forums, driving alignment across diverse stakeholder groups and securing buy-in for target-state designs. - Translate business strategy into architectural roadmaps, sequencing investment and change in a way that balances ambition with deliverability. **Business development & bid support** - Play a leading role in business development: identifying new opportunities, shaping propositions, and supporting or leading bids and tender responses for AI and technology engagements. - Contribute to proposal writing, articulating our capabilities and differentiators, including authoring technical and architecture sections of bid responses to client tenders and RFPs. - Support practice-level growth initiatives, including account planning, capability development, and go-to-market positioning for AI architecture and solutions engineering services. **Technical architecture — what you will design** You will be the design authority for enterprise AI platforms, able to reason across the full stack and make credible technology selections. We expect expert-level depth in several of the areas below and working knowledge across the rest. **AI platform & cloud architecture** - Architect AI and Data Platforms using a mix of technologies including Databricks, Snowflake, Claude Enterprise and the native services of Azure, AWS and GCP - selecting the right tool for the workload rather than defaulting to a single vendor. - Design for scalability, security, cost efficiency and multi-cloud / hybrid patterns, with a strong grasp of containerisation, infrastructure-as-code, event-driven architectures and CI/CD. - Balance technical excellence with delivery pragmatism and commercial realities, championing the path from prototype to production. **Enterprise AI ecosystem integration** - Bring awareness of the broader enterprise landscape and how AI integrates with core operational systems such as SAP, Salesforce, ServiceNow and other systems of record and engagement. - Design integration patterns that let AI capabilities interoperate safely with enterprise data, identity, workflow and process automation platforms – including MCP and A2A and how these capabilities enable an enterprise Agent mesh. - Understand the data, governance and change implications of embedding AI into mission-critical enterprise processes. **Orchestration & control plane** - Architect the orchestration and control-plane layer that governs how AI capabilities are exposed, routed and managed across the enterprise. - Design MCP, AI and LLM gateway layers for secure, observable and policy-controlled access to models and tools. - Design agent and skills registries — including recommending suitable technologies and patterns — to enable discovery, reuse and governance of agentic capabilities. - Define the control plane for routing, rate limiting, cost management, model selection and version control across multiple model providers. **AI solution design** - Design retrieval and reasoning architectures including RAG, corrective RAG (CRAG), and agentic patterns, selecting the right approach for a given business problem. - Architect agent harnesses and their components — planning, memory, tool use, state management and multi-agent coordination. - Design prompt strategies, context management and grounding approaches for reliable, production-grade LLM behaviour. **Evaluation, guardrails & responsible AI** - Design evaluation frameworks and guardrails that make AI systems safe, reliable and measurable — covering accuracy, safety, bias, and task success. - Embed responsible-AI and g