AI Architect - Talent Assessment and Capability Evaluation

Job Title: AI Architect - Talent Assessment and Capability Evaluation

Location: Hybrid (London/Paris) Preferred

Role Summary

The AI Architect will play a key role in evaluating organizational AI and data talent readiness. This role focuses on assessing technical capability, designing evaluation frameworks, and identifying readiness gaps across multiple AI and data teams. The architect will define what "good" looks like for AI practitioners at different maturity levels and ensure alignment with the enterprise's overall AI strategy.

This position is ideal for someone with strong architectural judgment, a deep understanding of modern AI ecosystems, and the ability to translate complex technical knowledge into clear evaluation criteria and insights.

Key Responsibilities

• Capability Framework Design: Develop and refine the assessment framework for AI and data technical roles - including architecture, MLOps, and applied AI functions.

• Question and Exercise Creation: Design technical and scenario-based questions to assess AI understanding, solution design ability, and architectural thinking.

• Talent Assessment: Evaluate existing AI talent through structured interviews, workshops, and scorecards to determine readiness and fit.

• Benchmarking: Define what "best-in-class" technical architecture looks like within the context of large enterprise AI adoption, referencing current industry practices.

• Cross-Functional Collaboration: Partner with program leads and consulting teams to ensure assessments align with capability goals and skill priorities.

• Gap Identification: Synthesize findings into insights that highlight capability strengths, weaknesses, and opportunities for targeted training or role refinement.

• Documentation: Create clear technical evaluation guides, rubrics, and reports summarizing assessment results and architectural capability maturity.

• Advisory Role: Provide thought leadership on AI architecture principles, tooling ecosystems, and governance relevant to enterprise AI scaling.

Qualifications

• 8+ years of experience in AI, data engineering, or software architecture with exposure to enterprise AI ecosystems.

• Strong grasp of AI system design principles (data pipelines, model lifecycle, orchestration, governance) even if not hands-on in implementation.

• Experience conducting technical interviews, designing assessments, or leading capability evaluations preferred.

• Excellent communication and stakeholder engagement skills, with the ability to translate technical complexity into actionable insights.

• Knowledge of cloud-based AI platforms (Azure, AWS, GCP) and enterprise MLOps environments a plus.

• Prior consulting or advisory experience within large organizations is advantageous.