AI Director

Overview

In this role you lead the end-to-end delivery of production-grade AI solutions within Futurice UK, guiding architecture, governance and client strategy. You will work closely with engineering teams and senior client stakeholders to mature AI from pilot to scalable production. You’ll shape technical direction, mentor the UK team, and grow capabilities across Europe. This is a hands-on leadership position that combines technical depth with client-facing impact and business development.

Pay / Benefits

• salary 90,000–130,000
• personal learning budget 1200/year
• flexible remote-first work
• private health insurance
• pension contributions 6%+
• wellbeing support 50/month via Juno

Responsibilities

• Set the technical direction for AI and data work across Futurice UK and ensure quality of builds
• Architect and build production AI systems including agentic apps, retrieval/knowledge systems, and data platforms
• Define evaluation metrics for AI systems covering quality, cost, latency and risk
• Advise client leadership on AI strategy, operating model, build-versus-buy, and production rollout
• Embed governance, privacy, security and responsible AI practices from the start
• Shape new opportunities through qualification, solution design, estimation and pitches
• Mentor, train and hire to share AI capability across the team
• Collaborate with the wider Futurice AI community in Europe
• Contribute to external profile via writing and public speaking
• Lead cross-disciplinary work with clients and internal teams during engagements

Key requirements

• Production AI systems experience with real deployments and production support
• Agentic architecture including multi-agent orchestration and memory/state management
• Context engineering with retrieval strategies, caching and cost trade-offs
• Evaluation and observability design and production tracing of agent behavior
• Retrieval and knowledge systems expertise including hybrid search and graph-based approaches
• Model selection/adaptation including fine-tuning and RL-based post-training
• Classical ML/data science skills (forecasting, optimization, causal inference, anomaly detection)
• Data and platform foundations (pipelines, lakehouse, data contracts, semantic layers, lineage)
• Cloud proficiency (AWS, Azure or GCP)
• AI security, tool-use, and governance including risk-aware design
• Software engineering fundamentals (Python, SQL, testing, code review)
• AI-assisted engineering with capable use of AI tooling in client contexts

• Strong communication with both engineering teams and senior clients
• Mentoring and developing others
• Ability to lead multi-disciplinary teams in changing requirements
• Production AI systems
• Agentic architecture
• Context engineering

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