AI & Intelligence Engineer AI Platforms

Luminous Executive Advisory & Strategy; Greater London; Hybrid; GBP 120,000 - 160,000; Full time

Job summary

Luminous Executive Advisory & Strategy is seeking a talented AI systems designer who can build integrated learning and reasoning systems. You will define how intelligence emerges and evolves in practice, combining learning, structure, and human judgment.

You will own the intelligence layer, including models, reasoning mechanisms, and evaluation approaches that convert data into insight and foresight, aligning AI outputs with real decision-making workflows.

Qualifications

• Strong background in machine learning or applied AI.
• Comfortable working across paradigms (ML, causal, symbolic).
• Interested in how intelligence behaves in real systems, not just benchmarks.
• Thoughtful about misuse, bias, and overreach.
• Excited by building AI systems that remain understandable and accountable.

Responsibilities

• Design and train ML models where learning adds clear value.
• Combine ML with statistical, rule-based or symbolic components.
• Develop models that operate over time (longitudinal, adaptive).
• Explore early agentic or semi-autonomous behaviors within strict boundaries.
• Define evaluation beyond accuracy (robustness, stability, interpretability).
• Integrate AI outputs into human decision-making workflows.

Skills

ML/Applied AI

Cross-paradigm thinking

Accountable AI

Responsible AI

Human–in–the–loop

Job description

Machine Learning Causal Models Systemic Reasoning

LOCATION

Remote | London

ROLE

Role Purpose

You build the AI itself - not just isolated models, but reasoning systems that integrate learning, structure, and human judgment.
This role defines how intelligence emerges, behaves, and evolves in practice.

What You'll Build & Own

You own the intelligence layer: models, reasoning mechanisms, and evaluation approaches that turn data into insight and foresight.

Key Responsibilities

• Design and train ML models where learning adds clear value
• Combine ML with:
• Statistical models
• Rule-based or symbolic components
• Develop models that operate over time (longitudinal, adaptive)
• Explore early agentic or semi-autonomous behaviors within strict boundaries
• Define evaluation beyond accuracy (robustness, stability, interpretability)
• Integrate AI outputs into human decision-making workflows

Who You Are

• Strong background in machine learning or applied AI
• Comfortable working across paradigms (ML, causal, symbolic)
• Interested in how intelligence behaves in real systems, not just benchmarks
• Thoughtful about misuse, bias, and overreach
• Excited by building AI systems that remain understandable and accountable