Technical Delivery Lead

Location/s: UK-wide
Recruiter contact: Laura Kennedy

Mott MacDonald is a global engineering, management, and development consultancy with over 20,000 employees across more than 50 countries and 140+ offices. 

We work across incredible global industries, delivering exciting work that is defining our future and making an important societal impact in the communities we serve. Our people power our performance – we succeed when they do. With countless opportunities to collaborate, learn, and grow, the possibilities for excellence are as varied as every individual. 

Whether you want to grow as a subject matter expert or broaden your experience with roles across our international community, you’re surrounded by global specialists who want to combine their expertise and champion you to be your best. As a proudly employee-owned business, we benefit our clients, our communities, and each other, investing in creating the right space for everyone to feel empowered, included, and valued. Whatever your ambition, Mott MacDonald is where people come to be brilliant.

About the business unit

Mott MacDonald’s support services are the driving force behind our organisation, enabling us to run efficiently and effectively. The team works collaboratively to offer specialist advice, best practice and technology to all areas of our business, specifically designed for our global reach.

Overview of the role

We are looking for a Technical Delivery Lead to join our Central Digital AI team.

This is a hands-on leadership role for an experienced project manager who combines disciplined delivery management with strong technical understanding and practical engineering capability. You will lead multidisciplinary teams that create AI agents and wider AI and automation solutions.

You will own complex digital and innovation projects and programmes end-to-end, from concept and business case through design, build and assurance to deployment, adoption and scaled operation. A central part of the role is turning promising pilots into robust, repeatable and scalable tools that deliver measurable value.

You will be expected to move between governance forums, delivery plans and stakeholder decisions and practical work such as prototyping agents, shaping workflows, connecting data and tools, reviewing technical designs, testing behaviour and resolving delivery blockers.

Key responsibilities and duties include:

End-to-end delivery leadership

• Shape the opportunity. Work with business users and domain specialists to define the problem, intended outcomes, scope, success measures, constraints and route to value.
• Lead from concept to scale. Own complex digital and innovation projects and programmes through discovery, business case, design, build, test, deployment, adoption, benefits realisation and transition into sustainable operations.
• Plan and control delivery. Establish integrated delivery plans, milestones, budgets, resource profiles, dependencies and critical paths; manage risks, assumptions, issues, decisions and change control. Understand when to escalate and strong focus on risks as well as unblocking challenges.
• Coordinate multidisciplinary teams. Bring together technical delivery, data science, software development, engineering, design, domain and operational expertise, ensuring that responsibilities and interfaces are understood.
• Assure adoption and service readiness. Plan testing, user acceptance, training, communications, deployment, operational support, monitoring and handover so that solutions are adopted and sustained.

AI agents and technical delivery

• Deliver applied AI and automation. Lead initiatives involving advanced analytics, machine learning, computer vision and intelligent automation, translating technical possibilities into practical, safe and valuable solutions.
• Build agents. Hands-on and prototype, configure, code, integrate, test and debug AI agents using low-code approaches. This includes prompt and instruction design, knowledge grounding, retrieval, tool and action integration, workflow orchestration, and evaluation.
• Select the right implementation path. Understand the strengths, limitations and trade-offs of low-code and pro-code AI capabilities, and choose an approach based on speed, control, integration complexity, security, scale, maintainability, cost and supportability.
• Design for responsible use. Embed data protection, information security, responsible AI, human oversight, traceability, evaluation and proportionate controls throughout the delivery lifecycle.

Scaling, governance and commercial value

• Turn pilots into products. Move prototypes beyond proof of concept by establishing repeatable delivery patterns, reusable components, production-grade controls, operational ownership, documentation and deployment playbooks.
• Enable reuse. Manage contractual, commercial and intellectual-property considerations with the relevant specialists so that solutions, components and learning can be reused appropriately across projects.

Stakeholder and people leadership

• Translate between audiences. Explain complex technical choices in clear business language and convert user and business needs into actionable technical and delivery requirements.
• Lead and develop people. Line manage and mentor delivery leads, setting clear objectives, giving constructive feedback, supporting career development and building delivery capability.
• Create an effective team culture. Coach team members, remove blockers and foster ownership, inclusion, learning, quality and pragmatic decision-making.

Candidate specification

Essential:

• Proven experience leading complex digital or innovation projects and programmes end-to-end, including successful transition from concept or pilot to scaled deployment.
• Strong hands-on project management capability across scope, plans, budgets, resources, governance, risk, dependencies, quality, change, suppliers, stakeholder communications and benefits.
• Demonstrable experience leading teams that build AI agents, together with practical experience personally building, configuring, integrating, testing and troubleshooting AI agents.
• Working knowledge of both low-code and pro-code AI delivery, with the judgement to select and combine them appropriately.
• Technical fluency across modern AI, data and software delivery, including APIs, integrations, cloud services, data pipelines, testing, deployment, observability, security and support.
• Understanding of applied AI and automation capabilities, including advanced analytics, machine learning and computer vision, and the controls needed to use them responsibly.
• Experience delivering solutions that connect physical systems or hardware and data capture with analytics, AI and software platforms.
• Evidence of turning prototypes into robust, repeatable, supportable and scalable products or services.
• Experience developing business cases, governance models, delivery structures, cost and resource models, and deployment or operating blueprints.
• Ability to address data, intellectual-property, responsible-AI, contractual and commercial considerations in partnership with relevant specialists.
• Experience coordinating multidisciplinary teams that include technical, analytical, engineering, domain and business contributors.
• Exc