The Role
Leads enterprise Data and AI product strategy while hands-on designing data architecture, scalable platforms, AI agents, automation workflows, and governance frameworks. Builds and scales LLM- and ML-powered products, feedback loops, and responsible AI controls. Partners with engineering, data science, consulting, business, and technology vendor teams; presents roadmaps and business cases; mentors technical talent; and drives adoption, ROI, and measurable business impact.
Summary Generated by Built In
About AbsoluteLabs
AbsoluteLabs is a specialist business and technology consulting firm with deep expertise in retail, consumer goods and other consumer industries. We combine industry advisory, process transformation and technology delivery to help clients achieve measurable commercial outcomes.
The Role
As Data & AI Product Lead, you will define the data and AI strategy, own the underlying data architecture, and lead the design, build and scaling of AI-powered products and agent ecosystems. You will turn business logic into automation, connect delivery and product data into centralised platforms, and ensure AI and data systems are governed, secure and genuinely adopted across the business.
This is a hands-on product and architecture leadership role, not a purely strategic or advisory position. You will build and own the data architecture and agent ecosystem end-to-end, working closely with engineers, data scientists, consultants and business teams to deliver tools that are practical, trusted and used at scale.
Key Responsibilities
AI and data strategy leadership
- Define and drive the Data & AI product strategy in alignment with overall business objectives.
- Identify, evaluate and prioritise AI, ML and data use cases across the enterprise.
- Track industry trends and emerging technologies (Generative AI, Agentic AI, ML, automation) to inform roadmap decisions.
- Present data and AI roadmaps and business cases to executive leadership.
Data architecture and platform design
- Define enterprise data architecture, including data models, pipelines and integration patterns that underpin AI and analytics products.
- Design scalable, secure data platforms spanning batch and streaming pipelines, APIs and operational systems.
- Establish data quality, lineage and governance standards across the data estate.
- Partner with engineering and platform teams to select and evolve cloud and AI/ML platforms.
Agent ecosystem and workflow automation
- Design and build a set of AI agents (for example scoping, estimation, risk) that work together as a single system.
- Define end-to-end workflows across the product lifecycle, ensuring smooth hand-offs between agents and humans and reducing manual steps and rework.
- Convert business logic, scoping rules, estimation models and governance policies into working, automated tools.
- Continuously improve agents and models based on real delivery and usage data.
Data and feedback loops
- Connect delivery and product data into a centralised data platform.
- Track key metrics such as estimate versus actual and margin versus plan.
- Use data and feedback loops to continuously improve products, agents and models over time.
Governance and responsible AI
- Establish governance frameworks for responsible, ethical and secure use of AI and data.
- Build tools that automatically check outputs against standards, flag risks early and enforce approval rules.
- Own AI and data system governance, including data privacy, model integrity, observability and cost management.
Team and stakeholder leadership
- Lead cross-functional teams to design, pilot and scale AI and data-powered products.
- Partner with consultants and business teams to understand real-world challenges, and with delivery centre teams to build and scale solutions globally.
- Build and manage relationships with internal stakeholders and external technology partners and vendors.
- Mentor and grow a team focused on data architecture, innovation and applied AI delivery.
- Measure and report on ROI and adoption metrics for AI and data initiatives.
Candidate Profile
Essential experience and capabilities
- 15+ years of experience in technology strategy, data architecture, innovation or AI/ML delivery, including 5+ years in a leadership role.
- Strong understanding of AI/ML concepts, Generative AI, and enterprise data and technology architecture.
- Hands-on experience with Large Language Models, agentic frameworks, MLOps and traditional AI and ML.
- Proven experience designing enterprise data architectures, including data models, pipelines, APIs and batch/streaming integration.
- Deep understanding of AI and data governance, including data privacy, model integrity, observability and cost management.
- Proficient in cloud technologies and enterprise-grade AI/ML and data platforms (for example AWS SageMaker, Bedrock, Azure ML, Databricks).
- Expertise in LLMs, including fine-tuning, RAG, prompt engineering, multi-agent collaboration, orchestration frameworks and alignment techniques.
- Demonstrated experience setting strategic technology direction and architecture vision for a large organisation, including target-state roadmaps.
- Proven track record leading cross-functional innovation programmes and driving change management and adoption at scale.
- Excellent stakeholder management, communication and executive presentation skills.
- Expert-level software engineering experience.
Desirable experience
- Experience with enterprise SaaS ecosystems and cloud platforms.
- Familiarity with AI governance, responsible AI practices and data privacy regulations.
- Background in consulting, delivery management or product innovation.
- Experience partnering with hyperscalers and technology vendors on build-versus-buy evaluations.
What Success Looks Like
Success in this role means building a thriving data and AI culture across the organisation, scaling AI and data capabilities to deliver measurable business impact, and growing a best-in-class portfolio of reusable data and AI solution components. It also means expanding the organisation's AI, ML and data architecture capability and talent over time.
What Kind of Person Succeeds Here
- Builds, not just designs.
- Thinks in systems and workflows, not isolated tools.
- Comfortable working with both engineers and consultants or business teams.
- Focused on solving real problems, not building “cool demos”.
- Can simplify complexity and make tools easy to use.
What's on Offer
- Competitive compensation and benefits.
- A high-visibility leadership role shaping enterprise-wide data and AI strategy.
- Ownership of the data architecture and agent ecosystem end-to-end, from strategy through to build and scale.
- Opportunity to grow and mentor a team focused on data architecture and applied AI.
- Career growth within a fast-moving, innovation-focused organisation.
Skills Required
- 15+ years of experience in technology strategy, data architecture, innovation, or AI/ML delivery
- 5+ years of experience in a leadership role
- Strong understanding of AI/ML concepts, Generative AI, and enterprise data and technology architecture
- Hands-on experience with Large Language Models, agentic frameworks, MLOps, and traditional AI and ML
- Experience designing enterprise data architectures, including data models, pipelines, APIs, and batch/streaming integrations
- Deep understanding of AI and data governance, data privacy, model integrity, observability, and cost management
- Proficiency in cloud technologies and enterprise AI/ML and data platforms such as AWS SageMaker, Bedrock, Azure ML, or Databricks
- Expertise in LLM fine-tuning, RAG, prompt engineering, multi-agent collaboration, orchestration frameworks, and alignment techniques
- Experience setting strategic technology direction and architecture vision for a large organization, including target-state roadmaps
- Track record leading cross-functional innovation programs and driving change management and adoption at scale
- Excellent stakeholder management, communication, and executive presentation skills
- Expert-level software engineering experience
- Experience with enterprise SaaS ecosystems and cloud platforms
- Familiarity with AI governance, responsible AI practices, and data privacy regulations
- Background in consulting, delivery management, or product innovation
- Experience partnering with hyperscalers and technology vendors on build-versus-buy evaluations
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The Company
What We Do
Absolute App Labs is a global software engineering and product development company serving startups, mid-sized businesses, enterprises, and global brands. It builds AI-first and AI-native digital products, including custom web and mobile applications, SaaS platforms, enterprise systems, and e-commerce solutions. Its capabilities include product modernization, cloud-native development, generative AI and LLM integration, data engineering, and intelligence platforms designed to scale.
