AI Trust Innovation Technologist

Job Description

The AI Trust Innovation Technologist strengthens SGS’s Digital Research & Ventures capabilities by actively building, testing, and analysing AI systems to develop credible independent validation and monitoring services. The role combines deep AI engineering expertise with venture-oriented innovation, evaluating emerging technologies and startups while translating hands-on experimentation into scalable Digital Trust validation solutions.

Key responsibilities:

• Analyse emerging AI technologies and real-world AI system architectures (, LLM-based systems, ML pipelines, multimodal systems) to identify where independent validation, testing, or monitoring by SGS is technically feasible and valuable.
• Technically assess AI risks — including robustness failures, bias/fairness issues, explainability limits, data integrity risks, cybersecurity vulnerabilities, and misuse scenarios (, deepfakes, hallucinations) — and translate them into potential validation or monitoring service opportunities.
• Develop, prototype, and evaluate AI validation approaches (, adversarial testing, dataset validation, interpretability methods, provenance/watermarking) to assess technical feasibility and scalability for Digital Trust services.
• Interpret AI regulations and standards (, EU AI Act, ISO/IEC AI standards, NIST AI RMF) and translate their technical implications into viable validation, monitoring, or independent evaluation approaches.
• Engage with universities, AI research labs, startups, and technology leaders to track cutting-edge AI system developments and explore collaboration, experimentation, and validation opportunities.
• Assess AI startups, tools, and platforms for technical maturity, architectural soundness, evaluation robustness, and strategic fit with SGS’s AI Trust ambitions.
• Provide technical insight and hands-on validation input for AI-related build–buy–partner–invest evaluations, including assessment of model architectures, evaluation methodologies, and system scalability.
• Contribute expert insight to Digital Trust marketing, thought leadership, and internal education on AI trust issues.
• Work cross-functionally with business lines, M&A, R&D, innovation teams, and IT to technically assess AI systems, prototype validation approaches, and support early-stage AI trust initiatives.
• Build and experiment with AI systems directly to deeply understand system behaviour, validation challenges, and potential service design implications.

Qualifications

Essential Experience:

• Advanced degree (Master’s or PhD) in AI, Machine Learning, Data Science, Computer Science, or a related field.
• 2–5 years of hands-on experience building, deploying, and validating AI/ML systems in production environments, startup environments, or advanced R&D settings.
• Strong understanding of AI validation and trust challenges (robustness, bias, explainability, AI security, misuse), grounded in practical AI system development experience.
• Practical experience applying AI evaluation and validation methods (, interpretability techniques, dataset validation, adversarial testing, red teaming, provenance or watermarking mechanisms).
• Experience working in research-driven, startup, or advanced AI R&D environments, with the ability to translate research concepts into working prototypes.
• Ability to translate deep technical understanding of AI system behaviour into scalable independent validation, monitoring, or assurance service opportunities.

Desirable Skills:

• Ability to technically analyse and diagnose AI system behaviour and identify validation, robustness, or monitoring gaps.
• Practical experience applying AI validation and evaluation approaches (, interpretability techniques, dataset validation, adversarial testing, red teaming, provenance or watermarking mechanisms).
• Understanding of major AI regulations and standards and their technical implications for AI system validation and monitoring approaches.
• Experience assessing AI startups, tools, or research from a technical architecture, evaluation, and system maturity perspective.
• Innovative, systems-oriented thinker able to translate technical AI validation challenges into scalable Digital Trust service concepts.
• Collaborative, hands-on, and comfortable working across research, engineering, business, and venture-building environments.
• Strong experimental mindset with the ability to rapidly prototype and test AI validation concepts.
• Familiarity with AI lifecycle management and MLOps concepts (, monitoring, drift detection, retraining pipelines) is an advantage.