• Develop, set-up, and execute CFD simulations using commercial or open-source solvers for high-speed aerospace system problems, including external and internal flows. • Create aerodynamic databases for vehicle trajectory simulations and performance models. • Perform analysis of internal flows of airbreathing propulsion flowpaths. • Develop and recommend design solutions to optimize aerodynamic performance and operability for high-speed systems. • Apply advanced data science techniques with machine learning to build surrogate models of CFD simulation results. • Support multidisciplinary design optimization (MDO) efforts. • Interpret and validate CFD simulation results against experimental data.

• Lead the development, industrialisation and improvement of Manufacturing Systems • Serve as the customer-facing representative of the ME function • Focus on delivery of non-recurring engineering projects, including New Product Introductions (NPI), Transfers of Work (ToW), Major Modifications (Mods), Technology Insertions and resolving manufacturing process related quality and delivery challenges • Lead deployed ME resources to deliver manufacturing systems in compliance with identified requirements • Ensure adherence to Zero Defect Manufacturing (ZDM) standards • Validate and approve the proposed definition of the Manufacturing System and its associated performance targets • Review output for technical risks and communicate these to the Programme • Own the Non-Recurring Engineering cost estimate for the ME technical content delivery • Deliver a Manufacturing System that fulfills all agreed stakeholder requirements • Govern and oversee a portfolio of Manufacturing Systems • Accountable for delivery of the non-recurring ME activities within a given programme or project • Drive technical leadership and direction using project management fundamentals • Maintain positive and functional working relationships with internal and external customers

• Lead customer engagements • Run technical discovery workshops with customer architects, data leaders, and AI teams, mapping data sources, MCP Workspace scoping, and agent tooling requirements • Own the scoping for AI deployments with clear acceptance criteria around agent accuracy, data coverage, and governance • Translate complex AI + data concepts into executive-ready architecture proposals; defend trade-offs to CxO-level stakeholders • Architect AI data connectivity end-to-end • Design solutions across Connect AI’s 350+ sources covering MCP server configuration, semantic context modeling, and governance integration • Architect agent orchestration using Connect AI Toolkits, defining which data, schemas, and actions each agent can access in production • Design governed access patterns for AI agents: RBAC, OAuth 2.1, semantic scoping, and audit-trail requirements • Define AI Best practices using agents, skills and LLMs to drive successful customer outcomes • Shape the evolution of the Connect AI platform through real-world deployments • Identify architectural and product gaps during live enterprise engagements and partner with Product and Engineering to define scalable solutions • Author technical specifications and implementation recommendations for Connect AI enhancements, including both features and core architectural improvements • Build reusable reference architectures, deployment patterns, and MCP blueprints that reduce implementation friction and accelerate future customer deployments • Translate recurring customer deployment challenges into scalable platform capabilities and architectural standards • Mentor FDEs on AI integration patterns, semantic data modeling, and customer-facing technical delivery • Serve as the first level technical escalation point for customer engagements • Partner with Product, Engineering, Solutions Engineering, and Customer Success on architectural standards • Codify field patterns into shared assets, playbooks, templates, sample agents, that the broader team can reuse (SE, CS, support etc.)

• Embed with customer teams to deploy Connect AI, configure MCP servers, connect data sources, and validate agent workflows in production • Build and test AI agent integrations using Connect AI Workspaces and Toolkits with correctly scoped data access • Configure semantic context and governance layers for accurate, permission-aware agent responses across connected sources • Build and ship AI integrations • Write production code in Python, Java, or C# connecting customer systems to Connect AI via MCP, REST, OData, and CData drivers • Implement the integration patterns and security configurations defined with the engagement architect • Contribute to and reuse the FDE team's reference architectures, blueprints, and sample agents • Build and deliver iterative solutions on a multi sprint cadence with weekly customer demos, validation checkpoints, and AI workflow refinement • Own deployment health for your engagements, monitor MCP performance, troubleshoot agent accuracy, and ensure a smooth Customer Success handoff • Triage and resolve production issues with appropriate escalation to the architect or product team • Document product gaps and agent behavior patterns from the field; feed prioritized insights to the Connect AI product team • Capture reusable patterns from your engagements, code snippets, deployment recipes, and troubleshooting notes that compound across the FDE team