This is a hybrid role and in the office three days a week.

There are no Visa sponsorship or Visa transfers.

About this Role

You are someone with demonstrated experience designing, deploying, and managing enterprise AI/LLM solutions in production, including RAG architecture, cloud platforms, vector databases, APIs, containerization, monitoring, governance, and security controls.

The Senior Lead AI & Risk Analytics Analyst is responsible for designing and operationalizing AI-enabled analytical solutions that enhance the identification, monitoring, and mitigation of emerging risks across the enterprise. This role combines expertise in risk analytics, prompt engineering, data visualization, and cross-functional collaboration to transform complex business and risk data into actionable intelligence.

The individual will develop advanced AI applications and agentic workflows, partner with engineers to design and deploy AI agents, and create executive-level dashboards, drill-through capabilities, and analytical visualizations that detect trends, root causes, concentrations, and emerging risk patterns. The role serves as a strategic advisor to business, risk, technology, and governance partners while helping establish scalable AI-driven analytical capabilities.

Key Responsibilities

AI Solution Development and Technical Architecture Implementation

• Design, develop, and deploy production-ready AI applications using generative AI techniques, including large language models (LLMs), retrieval-augmented generation (RAG), and agentic frameworks, to support risk identification, issue analysis, thematic reviews, and executing reporting.
• Build intelligence automation solutions (prompt libraries, governance standards, testing methodologies, reusable AI assets) that enhance data quality risk analysis and governance, and support business operational efficiency.
• Develop prompt engineering frameworks and fine-tuning strategies for domain-specific LLM applications.
• Create conversational AI interfaces, intelligent assistants and APIs to integrate AI applications into existing data risk platforms, for the full usage from non-technical stakeholders.

• Architecture and implement RAG pipelines for knowledge retrieval from structured and unstructured financial data sources. Evaluate AI outputs for accuracy, explainability, consistency, and adherence to enterprise risk and AI governance requirements.
• Optimize data storage and retrieval mechanisms for high-performance AI applications.
• Stay current with emerging AI technologies and evaluate their applicability to data quality risk governance needs.
• Work with compliance and risk management teams to ensure AI solutions meet regulatory and governance requirements.

Risk Analytics & Emerging Risk Detection

• Lead the analysis of large, complex datasets to identify emerging risks, systemic trends, control weaknesses, and root causes.
• Develop frameworks that leverage AI-generated insights to support proactive risk management and decision-making.
• Translate analytical findings into actionable recommendations that improve control effectiveness and risk mitigation.
• Create methodologies for detecting recurring patterns across issues, defects, incidents, controls, and other risk-related data sources.
• Maintain expertise in current and emerging risk trends and integrate those insights into analytical solutions.

Visualization & Business Intelligence

• Design and develop executive dashboards, scorecards, visual analytics, and drill-through reporting capabilities.
• Create interactive visualizations that enable leaders to investigate trends, concentrations, impacts, and emerging risk indicators.
• Build scalable reporting solutions that provide transparency into risk exposure, issue management performance, and remediation effectiveness.
• Define key risk indicators (KRIs), metrics, and thresholds to support proactive monitoring.
• Present complex analytical concepts through clear, actionable, and executive-ready storytelling.

Strategic Leadership & Stakeholder Management

• Lead complex, cross-functional initiatives involving Risk, Data Management, Technology, Compliance, Audit, and Business partners.
• Act as a trusted advisor to senior leaders on AI-enabled risk analytics strategies and opportunities.
• Communicate analytical findings, recommendations, and emerging risks to executive audiences.
• Influence strategic decisions regarding AI adoption, risk monitoring capabilities, and analytical maturity.
• Mentor analysts and contribute to the development of enterprise analytical best practices.

Required Qualifications:

• 7+ years of Data Management, Business Analysis, Analytics, or Project Management experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education.
• 3+ years of AI prompt engineering, AI agent development, advanced risk analytics and executive reporting, with strong capabilities in translating complex risk data into AI-enabled insights, dashboards, and decision-support solutions.
• Strong academic foundation in Computer Science, Data Science, Machine Learning, Artificial Intelligence, or related quantitative field.
• Hands-on experience deploying and supporting AI/LLM applications in production environments, including application architecture, scalability, monitoring, and operational support.

• Experience with AI orchestration frameworks, tools such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, and large language models (GPT, Claude, Llama, Gemini, etc.), or similar technologies.

• Experience implementing cloud-native AI solutions using Azure, AWS, or Google Cloud platforms, including enterprise AI services and APIs.

• Hands-on experience designing and implementing Retrieval-Augmented Generation (RAG) solutions utilizing vector databases, semantic search, and knowledge retrieval architectures.

• Experience developing and deploying APIs, microservices, and containerized applications using technologies such as FastAPI, Docker, Kubernetes, and CI/CD pipelines.

• Knowledge of AI/LLMOps practices including model evaluation, prompt optimization, version control, observability, performance monitoring, and governance controls.

• Understanding of AI security, responsible AI principles, model risk management, data privacy, explainability, and regulatory compliance within highly regulated environments.

Desired Qualifications:

• Proven track record of leading complex, cross-functional initiatives focused on data quality, issue remediation, and process/control improvements.

• Familiarity with data governance and data management tooling (e.g., data quality success metrics, data lineage, issue tracking) and experience in partnership with business and tech teams.

• Strong executive presence with the ability to influence stakeholders and drive alignment in a matrixed environment.

• Experience designing, implementing, or evolving enterprise data governance operating models.

• Advanced analytical and problem-solving skills, with the ability to structure ambiguous challenges and deliver actionable insights.

• Strong proficiency in Python, SQL, and front-end development.

Posting End Date:

24 Jul 2026

*Job posting may come down early due to volume of applicants.