AI & Automation

A RegTech Analyst works at the intersection of regulatory compliance and technology, helping organisations implement and manage software solutions that automate, streamline, or enhance their compliance processes. Day-to-day work involves analysing regulatory requirements and mapping them to technology capabilities, supporting the evaluation and onboarding of RegTech tools, maintaining regulatory change management processes, and working with compliance, IT, and data teams to ensure technology solutions meet both legal requirements and business needs. The role requires a combination of compliance knowledge, analytical thinking, and comfort with technology platforms — making it one of the most forward-looking entry points in the compliance market. RegTech Analyst roles exist in two main contexts: within regulated firms (banks, insurers, payment institutions) who are implementing RegTech solutions to improve their compliance operations, and within RegTech vendors who need analysts to support product development, client implementation, and regulatory mapping. Both offer strong career foundations, with vendor roles typically providing faster exposure to a variety of regulatory problems and client environments. The UK is one of the world's leading RegTech hubs, supported by the FCA's Innovation Hub and Regulatory Sandbox, which creates a dynamic job market for early-career professionals in this space.

An AI Prompt Engineer designs, tests, and refines the instructions — known as prompts — that guide large language models (LLMs) to produce accurate, relevant, and safe outputs for a specific use case. Day-to-day work involves writing and iterating on prompts for production AI applications, running structured evaluations to measure how well prompts perform against defined quality criteria, collaborating with software engineers and product managers to integrate AI outputs into products and workflows, and documenting prompt strategies, failure modes, and evaluation results. The role requires both technical curiosity — understanding how LLMs behave and why — and strong written communication skills, since the primary tool is language itself. AI Prompt Engineering is one of the newest roles in technology, emerging alongside the mainstream adoption of LLMs from 2022 onwards. In the UK, it exists primarily at technology companies, AI-native startups, large enterprises running AI transformation programmes, and consulting firms advising clients on AI adoption. The role is still evolving — some organisations treat it as a standalone specialism, others embed it within product, data science, or content teams. Entry-level positions focus on prompt iteration and evaluation, while senior practitioners work on systematic evaluation frameworks, retrieval-augmented generation (RAG) architecture, and model selection decisions.

An MLOps Support Analyst helps an organisation deploy, monitor, and maintain machine learning models in production, bridging the gap between the data science team that builds models and the engineering and operations teams that keep systems running reliably. Day-to-day work involves monitoring model performance metrics to detect when predictions are degrading, supporting incident investigations when model behaviour changes unexpectedly, maintaining the pipelines that retrain and redeploy models, documenting model behaviour and known failure modes, and working with data scientists and engineers to improve the reliability of ML infrastructure. The role requires a combination of analytical rigour — understanding what makes a model's output trustworthy — and operational discipline around software systems and pipelines. MLOps (Machine Learning Operations) as a discipline emerged from the recognition that deploying a model is only the beginning: production ML systems require ongoing monitoring, retraining, and maintenance to remain accurate as the world changes around them. Entry-level MLOps Support Analyst positions are relatively rare — the function is often absorbed by data scientists or platform engineers in smaller organisations — but as ML deployments scale, dedicated operational support becomes essential. The role exists primarily at technology companies, financial services firms with large ML estates, and organisations running AI transformation programmes. It is a strong entry point into a broader ML engineering or data science career.

An Automation / RPA Analyst identifies, designs, and supports the deployment of software robots and automated workflows that replace or augment repetitive manual processes in an organisation. Day-to-day work involves working with business teams to map current processes, assess their suitability for automation, document requirements for robotic process automation (RPA) tools such as UiPath, Automation Anywhere, or Blue Prism, supporting the configuration and testing of bots, and monitoring deployed automations to ensure they continue to function correctly as underlying systems change. The role sits at the intersection of business analysis and technical implementation — Automation Analysts must understand processes well enough to redesign them, and understand the tooling well enough to specify and validate solutions. Entry-level positions typically focus on process discovery, documentation, and testing support, progressing toward bot development and ownership of the automation pipeline. The function exists across financial services, healthcare, the public sector, utilities, and retail — anywhere manual, rule-based processes are consuming significant operational capacity. Organisations running large automation programmes often employ dedicated RPA analysts alongside a Centre of Excellence that sets standards and governs the pipeline. Analysts who develop both process redesign skills and hands-on RPA tool proficiency progress fastest, as the ability to bridge business and technical perspectives is the core value of the role.

An AI Quality / Testing Analyst designs and executes the evaluation processes that ensure AI systems — particularly those using large language models or machine learning models — behave reliably, safely, and as intended before and after deployment. Day-to-day work involves writing test cases that probe AI system behaviour across a wide range of inputs including edge cases and adversarial examples, running structured evaluations to measure output quality against defined criteria, documenting failure modes and unexpected behaviours, collaborating with prompt engineers and ML engineers to investigate root causes, and maintaining evaluation datasets and benchmarks. The role requires a combination of systematic QA discipline, curiosity about AI system behaviour, and enough technical literacy to understand what is being tested and why it might fail. AI Quality Testing is one of the newest specialisms in the technology sector, emerging from the realisation that conventional software testing methods — which verify deterministic outputs — are insufficient for AI systems whose outputs are probabilistic, context-dependent, and difficult to fully specify in advance. Entry-level positions typically focus on test case design, manual evaluation, and maintaining test sets, with progression toward automated evaluation frameworks, red-teaming, and evaluation methodology ownership. The role exists primarily at AI companies, technology firms with significant AI deployments, financial services organisations using ML in regulated decision-making, and consulting firms advising on AI governance.