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Citi • Northern Ireland, United Kingdom
Role & seniority: AI Governance & Model Risk Specialist (Senior/Lead level)
Stack/tools: AI/ML lifecycle governance; risk management and compliance frameworks; data analysis methods; performance testing methodologies and tools; coordination with Model Risk Management (MRM)
Track and manage AI lifecycle dates (limitation expirations, policy decisions, scheduled model updates); prepare and submit risk artifacts per policies/regulations
Coordinate review sessions with MRM and stakeholders to ensure governance, risk, and compliance standards are met
Conduct SME reviews of AI object outputs; perform fine-tuning and comprehensive performance tests; analyze results to drive optimizations and re-testing with development teams
Proven experience in AI/ML model development, operations, or testing
Strong understanding of AI/ML lifecycle and deployment challenges
Familiarity with risk management, governance, and regulatory frameworks
Proficiency in data analysis; ability to interpret complex datasets
Experience with performance testing methodologies/tools
Excellent analytical, problem-solving, and critical-thinking abilities
Strong communication, collaboration, and stakeholder management; able to work independently and cross-functionally
Regulated-industry domain experience
Additional exposure to compliance artifacts, audits, or regulatory approvals
Location & work
Proactively track and manage critical dates related to AI model lifecycles, including limitation expirations, policy decisions (PDs), and scheduled model updates. Prepare, review, and submit all necessary risk artifacts, ensuring strict adherence to internal policies and external regulatory requirements. Coordinate and facilitate review sessions with Model Risk Management (MRM) and other pertinent stakeholders to ensure AI models consistently meet established governance, risk, and compliance standards. Perform detailed manual Subject Matter Expert (SME) reviews of AI object outputs to rigorously assess and confirm their accuracy, relevance, and alignment with defined business objectives. Execute fine-tuning processes for AI objects to optimize their performance, accuracy, and responsiveness based on ongoing monitoring results, feedback loops, and evolving requirements. Design, develop, and execute comprehensive performance tests for AI objects to evaluate their scalability, responsiveness, stability, and resource utilization under various load or hyper-parameter conditions. Analyze detailed test results to pinpoint performance bottlenecks, potential failure points, and areas for optimization within AI systems. Collaborate closely with AI development teams to implement performance improvements, conduct re-testing, and ensure solutions meet or exceed established performance benchmarks and non-functional requirements. Proven experience in AI/Machine Learning model development, operations, or testing. Strong understanding of AI/ML lifecycle, concepts, and deployment challenges. Familiarity with risk management, governance, and compliance frameworks, preferably within a regulated industry. Proficiency in data analysis tools and techniques, with the ability to interpret complex datasets. Experience with performance testing methodologies and tools. Excellent analytical, problem-solving, and critical thinking skills. Strong communication, collaboration, and stakeholder management abilities. Ability to work independently and as part of a cross-functional team in a fast-paced environment. ------------------------------------------------------ For complementary skills, please see above and/or contact the recruiter. ------------------------------------------------------