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 skills.

Ability to communicate between business and technical teams.

Strong organisational and coordination skills.

Ability to work across multiple technology teams including application, infrastructure, architecture and support teams.

Strong team player with good interpersonal sk