About this role
Launch Your Career with Frost & Sullivan
At Frost & Sullivan, we believe that the future belongs to curious minds, innovative thinkers, and problem-solvers who are eager to make an impact. We are inviting applications from postgraduate students, recent graduates, and early-career professionals with up to two years of experience to join our growing global teams across various business, technology, research, consulting, AI, data, and corporate functions.
The Opportunity
Frost & Sullivan is looking for intern roles supporting MetaBrain. The work combines applied AI, business understanding, structured knowledge, quantitative models and trustworthy engineering to transform research and advisory into reusable software-enabled services. Build more than a demonstration. Work with industry researchers, advisors and engineers to turn AI capability into tested decision-intelligence software that enterprises can use.
Role Overview
Show how your technical work supports a business problem. Research, consulting or advisory experience is preferred but not mandatory. Academic projects, thesis and reproducible research implementations are valid evidence; internships do not require prior full-time employment.
Engineer and test the controls that make AI-assisted research and advisory reliable, traceable and appropriately restricted. Work across engineering, data science and domain teams so trustworthy behaviour is evidenced in the system, not only described in policy.
Proposed engagement
Stipend: Yes, paid internship.
Duration: Preferably six months, possibility of an extension up to 12 months based on performance and where academic arrangements and work authorization permits are in place.
Full-time Conversion: Depends on assessed performance, a suitable vacancy and eligibility; it is not guaranteed.
Essential requirements
Master's/PhD study or qualification in AI, computer science, data science, cybersecurity or a closely related field with substantive AI training. Practical Python, software testing and basic understanding of authentication, authorization and data handling. An example of a project or research in trustworthy AI, model evaluation, privacy, security, fairness, robustness or explainability.
Preferred: AI assurance frameworks, automated testing, threat modelling, privacy engineering, policy-as-code, data lineage, monitoring or regulated enterprise workflows. Research-integrity or advisory exposure is valuable; a policy-only background without implementation evidence is insufficient for the Engineer role.
Key responsibilities
· Map risks and evidence: Document use-case harms, datasets, model versions, limitations and approval owners. Translate relevant data-use, confidentiality and responsible-AI requirements into testable control objectives with legal/privacy and security mentors.
· Implement trust controls: Develop tests and components for role-based access, tenant separation, source provenance, audit logs, data masking and retention. Link released outputs to approved sources, configurations, evaluation evidence and review decisions.
· Test unsafe and unreliable behaviour: Assess unsupported claims, faulty citations, bias, inappropriate disclosure and unauthorized tool actions. Run approved adversarial tests in controlled environments; distinguish severity, reproducibility and residual risk.
Support release and operations: Automate assurance checks, document findings and track remediation. Contribute to release evidence, incident escalation, monitoring and rollback exercises; explain control limitations clearly to business owners.