Application / Software Systems Analysis – GenAI / Agentic AI / LLM

Client: Bank Sector Client: Primary Focus: Application / Software Systems Analysis – GenAI Secondary Exposure: Technology Solution Architecture / Enterprise Integration Domain/Project: Global Markets, Capital Markets Banking Technology, and Market Risk Technology. Role Overview We are looking for an experienced System Analyst with Generative AI (GenAI) and global markets / capital markets banking technology experience to support the analysis, solution design and delivery of enterprise GenAI and Agentic AI solutions. This is a techno-functional role sitting between business users, technology teams and solution/architecture teams. The successful candidate will analyse business requirements, facilitate requirements workshops, translate business needs into functional and technical solution designs, and support the implementation of GenAI / Large Language Model (LLM) solutions within an enterprise banking environment . The role requires a combination of: • Systems Analysis • Banking Technology / Banking Processes • GenAI / Large Language Models • Agentic AI / AI Agents • Solution Design • Stakeholder Management • Enterprise Technology Integration Key Responsibilities • Gather, analyse and translate business requirements into functional and technical solution designs . • Conduct user requirements workshops with business users, technology teams and other stakeholders. • Understand existing business processes, system workflows and transaction flows and determine how proposed solutions can meet business requirements. • Perform a hands-on techno-functional System Analyst role , analysing business and system issues and recommending practical technical, functional and process solutions. • Translate business requirements into: • Functional requirements • System requirements • Solution designs • Process changes • GenAI / AI use cases • Support the design and implementation of Generative AI (GenAI) and Agentic AI solutions within enterprise banking environments. • Work with technology, infrastructure, application support, architecture and other teams to deliver end-to-end system solutions. • Evaluate how Large Language Models (LLMs) and GenAI technologies can be integrated into existing business processes and applications. • Analyse and understand Agentic AI architectures , including agent patterns, agent workflows and interactions between AI agents and enterprise systems. • Support solution design involving: • Large Language Models (LLMs) • Retrieval-Augmented Generation (RAG) • Agentic AI / AI Agents • LangChain • LangGraph • Model Context Protocol (MCP) • Prompt Engineering • LLM Model Integration • Assess and evaluate LLMs such as OpenAI, Gemini, Llama, DeepSeek or similar models for enterprise use cases. • Collaborate with technical architects and engineering teams to ensure solutions align with the organisation's technical architecture, system standards and enterprise requirements . • Participate in the design of scalable enterprise GenAI architectures and solutions . • Collaborate with stakeholders to understand priorities, business needs, system improvements and technology requirements. • Build strong relationships with business users and technology stakeholders and manage expectations throughout the solution delivery lifecycle. Requirements Education & Experience • Bachelor's Degree in Computer Science, Information Technology, Information Systems, Engineering or a related discipline . • 6+ years of relevant experience in Systems Analysis, Technology Delivery, Business Systems Analysis or related technology roles. • Experience working within Banking, Financial Services or Financial Institution technology environments is strongly preferred. • Good understanding of banking processes, banking systems and enterprise technology environments . Systems Analysis & Solution Design Strong experience in: • System Analysis / Systems Analysis • Business Requirements Analysis • Functional Requirements • Requirements Gathering • Requirements Workshops • Solution Design • Functional Solution Design • Business Process Analysis • System Process Analysis • Stakeholder Management • Enterprise Systems • Technology Delivery Candidates should be comfortable working between business users and technical teams , translating business requirements into workable technology solutions. Generative AI / LLM Knowledge Good understanding and practical exposure to: • Generative AI / GenAI • Large Language Models (LLMs) • Agentic AI • AI Agents • Agent Patterns • Agent Workflows • Retrieval-Augmented Generation (RAG) • LangChain • LangGraph • Model Context Protocol (MCP) • Prompt Engineering • LLM Architecture • Model Evaluation • Model Integration • Context Management • LLM / AI Workflows Candidates should understand how these technologies can be integrated into enterprise applications and business processes . LLM / Model Experience Experience evaluating, integrating or working with LLMs such as: • OpenAI • Gemini • Llama • DeepSeek • Other commercial or open-weight Large Language Models Fundamental understanding of: • LLM / model architecture • Prompt Engineering • Model selection and evaluation • Model integration • Fine-tuning concepts Deep AI research or model-development experience is not required, but candidates should be sufficiently technical to understand GenAI solution designs and work effectively with engineering and architecture teams. Technical Skills Exposure to the following would be advantageous: • Python • LangChain • LangGraph • RAG • Model Context Protocol (MCP) • Agentic AI frameworks • Enterprise GenAI platforms • OpenShift • Containerised application environments • GenAI workflow / orchestration technologies Key Competencies • Strong analytical and problem-solving skills. • Ability to question proposed solutions and understand the underlying business drivers. • Strong communication and stakeholder management sk…