The AI Workflow & Automation Engineer will join the Customer Service Platforms team at IG Group, a FTSE 100 fintech company. This role is responsible for designing and optimizing AI-driven workflows and automation to enhance digital support experiences across chat, messaging, and self-service channels. The primary goal is to translate complex business processes and policies into structured AI guidance to reduce manual handling and improve resolution times. Responsibilities: Design, build, and maintain AI-powered workflows, prompts, and automated responses within platforms like Intercom. Translate business rules and troubleshooting steps into structured AI guidance. Analyze conversation data and KPIs to optimize AI accuracy, tone, and resolution rates. Collaborate with product, operations, and compliance teams to ensure AI content is accurate and compliant. Maintain documentation, testing, and version control for all automation changes. Requirements: Experience in digital support automation, conversational design, or AI-enabled workflows in customer-facing environments. Ability to break down complex processes into logical steps. Excellent written communication skills. Analytical mindset with experience using support data to drive improvements. Familiarity with digital support platforms such as Intercom, Genesys, or similar bot/agent-assist tools. Comfort working in a regulated environment with high attention to risk and detail. Working Arrangement: This is a hybrid role requiring three days in the office.
For full details about this role, click “Apply on company site” to view the employer’s original posting.
A focused preparation guide, built around this job’s responsibilities and requirements.
✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
Understand the role
What matters most
Designing Structured AI Workflows
The core responsibility is translating complex business rules, policies, and troubleshooting steps into logical, automated guidance within platforms like Intercom. Success requires the ability to map intricate human-led processes into structured, machine-readable formats that maintain high accuracy and consistent tone.
Data-Driven Performance Optimization
You must continuously analyze conversation data, deflection rates, and resolution KPIs to iterate on AI behavior. This requires a strong analytical mindset to identify friction points in the customer journey and refine prompts or workflows to improve containment and overall support efficiency.
Cross-Functional Compliance and Risk Management
Operating in a regulated fintech environment necessitates close collaboration with compliance and operations teams. You must ensure all automated responses are not only efficient but also strictly compliant with financial policies, requiring meticulous documentation, rigorous testing, and version control for every change.
Likely challenges to think through
Balancing Automation with Regulatory Rigor
In a FTSE 100 fintech, the margin for error in automated communication is extremely low. The challenge is to increase automation speed and deflection rates without compromising accuracy or compliance. Prepare by demonstrating how you have managed risk in previous automation projects and how you implement testing frameworks to catch potential AI hallucinations or policy deviations.
Translating Complexity into Conversational Simplicity
Financial services processes are often inherently complex, yet they must be delivered through chat and messaging interfaces that require brevity and clarity. Prepare by practicing how you break down multi-step, technical procedures into simple, user-friendly conversational flows that guide a customer to a resolution without overwhelming them.
03
Practice your answers
Technical practice
Designing Conversational Workflows
What to coverExplain your methodology for mapping complex business processes into logical, automated steps. Focus on how you ensure the flow remains user-friendly while meeting strict business requirements.
Prompt Engineering for Customer Support
What to coverDescribe your approach to crafting prompts that maintain a consistent brand tone and ensure accuracy. Discuss how you handle edge cases or ambiguous user queries to prevent incorrect AI responses.
Optimizing AI Performance via KPIs
What to coverWhich metrics do you prioritize when evaluating the success of an automated support workflow? Explain how you use data from platforms like Intercom to identify bottlenecks and improve containment or resolution rates.
Managing AI in Regulated Environments
What to coverHow do you balance the need for automation efficiency with the strict compliance and risk requirements of a fintech company? Discuss your process for incorporating compliance reviews into the development lifecycle.
Version Control and Testing for Automation
What to coverDetail your strategy for maintaining documentation and version control when updating AI workflows. Explain how you conduct testing to ensure changes do not negatively impact existing support experiences.
Platform Integration and Agent Assist
What to coverCompare the challenges of designing self-service bots versus agent-assist tools. Suggestion: Focus on how you tailor the information architecture to support both the end-user and the internal support agent.
Data-Driven Iteration
What to coverDescribe a scenario where you used conversation insights to pivot an existing automation strategy. Explain how you validated that the changes led to measurable improvements in customer experience.
Behavioural practice
Cross-Functional Collaboration
What to coverDescribe a time you had to work with product, operations, and compliance teams to launch an automation project. How did you reconcile conflicting priorities to ensure the final output was both efficient and compliant?
Handling Complex Process Decomposition
What to coverProvide an example of a highly complex business process you successfully automated. Explain the steps you took to simplify the logic without losing the necessary detail required for accurate resolution.
Managing Risk and Accuracy
What to coverTell me about a time you identified a potential risk in an automated workflow before it went live. What actions did you take to mitigate that risk while maintaining the project timeline?
Adapting to Feedback
What to coverGive an example of when performance data or stakeholder feedback indicated that an automated workflow was underperforming. How did you analyze the issue and what specific changes did you implement to optimize it?
02
Sharpen your knowledge
Topics to prepare
Conversational Design Principles
Focus on creating logical, user-centric dialogue flows that translate complex business rules into clear, actionable AI guidance. Exercise: Take a multi-step troubleshooting process from a common service and map it into a decision tree or flowchart that minimizes user effort and maximizes resolution accuracy.
Risk and Compliance in AI
Understand the constraints of operating in a regulated fintech environment where AI outputs must be accurate and compliant. Exercise: Review a set of hypothetical customer support policies and identify potential 'hallucination' risks or compliance pitfalls that could occur if an AI bot were to interpret these rules incorrectly.
Skills to revise
Prompt Engineering for Support Automation
Develop the ability to craft prompts that maintain consistent tone and accuracy within platforms like Intercom. Exercise: Draft a series of system prompts for a bot that must handle sensitive financial queries, ensuring the tone remains professional, concise, and strictly adheres to predefined safety guardrails.
Data-Driven AI Optimization
Use conversation data and KPIs such as deflection rates and resolution times to iterate on AI performance. Exercise: Create a mock dashboard or report that analyzes a set of conversation logs to identify three specific areas where the bot failed to resolve a query, and propose a specific workflow adjustment to improve future performance.
Technical Documentation and Version Control
Maintain clear records of automation changes, testing results, and logic updates. Exercise: Document a hypothetical update to an existing AI workflow, including the rationale for the change, the testing steps performed to validate the update, and a versioning note for future reference.
04
Your preparation checklist
Prepare a conversational design case study
Create a portfolio piece demonstrating how you translated a complex, multi-step business policy into a logical, user-friendly automated workflow. Highlight how you structured the prompts and decision trees to ensure accuracy and compliance.
Develop a data-driven optimization framework
Outline your methodology for analyzing support conversation data. Explain how you identify KPIs like deflection rates and resolution times, and provide an example of how you used these metrics to iterate on a bot's performance or tone.
Review regulatory compliance in AI
Prepare to discuss your experience or approach to working within highly regulated environments. Focus on how you balance automation efficiency with the strict risk, accuracy, and documentation requirements necessary in the fintech sector.
Draft technical documentation samples
Prepare a sample of your technical documentation for an automated workflow. This should include version control logs, testing protocols, and clear instructions for stakeholders on how the AI logic handles specific edge cases.
Formulate strategic questions for the team
Prepare thoughtful questions regarding the current integration of Intercom or Genesys at IG Group, the primary compliance hurdles the team faces, and how the Customer Service Platforms team measures the success of new AI initiatives.
Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy
Share this opportunityKnow someone who would be a great fit?