AI Tech Lead

Euromonitor International leads the world in data analytics and research into markets, industries, economies and consumers. We provide truly global insight and data on thousands of products and services; we are the first destination for organisations seeking growth. With our guidance, our clients can make bold, strategic decisions with confidence.

The AI Technical Lead is responsible for the strategy, governance, adoption, and day-to-day management of internally used AI platforms and tooling across the Technology organization. This role combines technical leadership, platform ownership, stakeholder engagement, and change management to ensure AI capabilities deliver measurable value while remaining secure, cost-effective, and aligned with organizational standards. 

The AI Technical Lead acts as the primary technical authority for internal AI tooling, including AI assistants, developer productivity tools, model access platforms, and related services. They are responsible for evaluating new capabilities, validating features through structured testing, defining adoption approaches, and ensuring governance frameworks are established and maintained. 

The role requires a strong understanding of cloud FinOps principles to manage AI-related expenditure effectively, balancing innovation with responsible cost management. The AI Technical Lead provides guidance on consumption patterns, licensing, optimization opportunities, and value realization across AI platforms. 

Working closely with engineering teams, architecture, security, compliance, and business stakeholders, the AI Technical Lead develops standards, best practices, training materials, and enablement programs that help teams leverage AI responsibly and effectively. They facilitate workshops, coaching sessions, proof-of-concept initiatives, and community activities that accelerate AI literacy and practical adoption throughout the Technology department. 

The AI Technical Lead owns the backlog and roadmap for internal AI tooling, gathering requirements and feedback from stakeholders, translating them into well-defined work items, and driving delivery of enhancements that improve productivity, knowledge sharing, engineering effectiveness, and business outcomes. They ensure governance, security, compliance, and operational requirements are incorporated into all AI initiatives. 

When new AI technologies emerge, the AI Technical Lead assesses their applicability to the organization, provides recommendations on adoption, and helps establish controls that enable innovation while managing risk. 

AI Strategy & Adoption

Define and execute the roadmap for internal AI tooling and capabilities; identify opportunities to improve productivity and business outcomes through AI; evaluate emerging technologies and recommend adoption approaches; and align AI initiatives with organizational objectives and technology strategy. 

AI Platform Ownership 

Own the lifecycle management of internal AI platforms and tools (development, deployment, monitoring); coordinate feature evaluations, pilot programs, rollout activities, and operational improvements; manage platform relationships and licensing considerations; and ensure services are reliable, secure, and delivering measurable value. 

AI Governance & Risk Management 

Establish and maintain governance frameworks, standards, policies, and guardrails for secure AI usage; collaborate with InfoSec, Data Protection, Legal, and Architecture teams to ensure AI adoption aligns with organizational requirements; and promote responsible, transparent, and ethical use of AI technologies. 

FinOps & Cost Management 

Monitor AI platform consumption and costs; analyse usage trends and value realization; identify optimization opportunities; support budgeting and forecasting activities; provide recommendations for efficient resource utilization; and ensure AI investments are managed responsibly and sustainably. 

Testing, Evaluation & Quality Assurance 

Lead structured testing and validation of new AI capabilities and model releases; evaluate functionality, performance, accuracy, usability, security, and business value; document findings and recommendations; and ensure new capabilities meet agreed standards before wider adoption. 

Training, Workshops & Community Building 

Design and deliver AI training programs, workshops, demonstrations, and hands-on learning sessions tailored to Technology teams; develop learning resources and best practice guidance; establish communities of practice; and encourage knowledge sharing across the organization. 

Backlog, Delivery & Stakeholder Alignment 

Own and prioritise the backlog and roadmap for AI tooling and initiatives; gather requirements and feedback from stakeholders; manage dependencies, risks, and delivery plans; communicate priorities and outcomes clearly; and ensure delivered capabilities address real user needs. 

Continuous Improvement 

Use data, feedback, and adoption metrics to drive continuous improvement; identify barriers preventing effective AI usage; introduce improvements to tooling, processes, and governance; and continuously enhance the user experience. 

Documentation & Standards 

Ensure clear, accessible documentation covering AI tooling, governance requirements, usage guidelines, best practices, operating procedures, and decision records; support self-service enablement and maintain knowledge repositories. 

Technical Leadership 

Provide technical leadership and subject matter expertise relating to AI platforms, model capabilities, integrations, prompt engineering approaches, automation opportunities, and adoption patterns; guide technical decision-making and help teams maximize value from AI technologies. 

• Strong understanding of Generative AI technologies, Large Language Models (LLMs), AI assistants, and enterprise AI adoption approaches. 

• Experience managing AI-enabled platforms and services within a medium to large organization. 

• Experience in Platform Engineering 

• Understanding of cloud FinOps principles and experience managing technology consumption costs. 

• Experience evaluating, testing, and validating new technologies or platform capabilities. 

• Knowledge of AI governance, risk management, compliance, information security, and responsible AI principles. 

• Experience building and delivering technical training, workshops, or enablement programs. 

• Strong stakeholder management and communication skills. 

• Knowledge of Microsoft Copilot, Azure OpenAI, Azure AI Services, Microsoft Fabric AI capabilities, GitHub Copilot, Claude, or equivalent platforms. 

• Understanding of software engineering practices and modern technology delivery methods. 

• Ability to analyse usage data, adoption trends, and business outcomes to support decision making 

Advantageous: 

• Certification in FinOps framework. 

• Microsoft Applied Skills or certifications related to Azure AI services. 

• AI Governance or Responsible AI related certification. 

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Why work for Euromonitor?

Our Values:

We seek individuals who act with integrity

We look for candidates who are curious about the world

We feel that as a community, we’re stronger together

We seek to enable people to feel empowered

We welcome candidates who bring strength in diversity

International: We have a multinational workforce and communicate daily across our 16 global offices.

Hardworking and Sociable: Our staff balance hard work with enjoyment, offering flexible hours and regular social events, including after-work meetups, summer and Christmas parties.

Committed to Making a Difference: Our Corporate Social Responsibility Programme provides two volunteering days annually; donation amounts for new starters and supports local and international charities through various initiatives.

Excellent Benefits: We offer competitive salaries, private health insurance, and generous holiday allowances, amongst much more!

Opportunities to Grow: We provide extensive training and development, promoting from within and across departments, and rewarding talent.

Equal Employment Opportunity: Euromonitor International does not discriminate based on race, colour, religion, sex, national origin, political affiliation, sexual orientation, gender identity, marital status, disability, genetic information, age, membership in an employee organization, or other non-merit factors.