Customer AI Engineer

Job Description

Job Role:

Customer AI Engineer Team:
Song AI & Data - AI & Modelling Craft, UK&I

Location:

London / Manchester

Career Level:

Consultant (L9) We Are

Accenture Song accelerates growth and value for our clients through sustained customer relevance. Our capabilities span ideation to execution: growth, product and experience design; technology and experience platforms; creative, media and marketing strategy; and campaign, content and channel orchestration. With strong client relationships and deep industry expertise, we help our clients operate efficiently and sustainably through the unlimited potential of imagination, technology and intelligence.

Within Accenture Song sits AI & Data, the practice that builds the data-led intelligence behind the customer work. Song AI & Data helps organisations unlock value from data, analytics and AI by creating more relevant, personalised and effective customer experiences. Our expertise spans customer insight, data strategy and platforms, advanced analytics, performance optimisation, and AI (including generative AI and agentic AI) transformation.

You will join the Song AI & Data UK practice, within the AI & Modelling Craft: a community of data scientists, AI engineers, modellers and solution architects focused on applying AI, machine learning and advanced analytics to solve customer and growth challenges. Our teams work across the full lifecycle, from identifying opportunities and designing solutions through to building, deploying and operating AI products that deliver measurable business value.

As a Customer AI Engineer, you will design, build and deploy machine learning, generative AI and agentic AI solutions that help our clients better understand, serve and grow their customers. This is a hands‑on engineering role where you will work across the full delivery lifecycle, from understanding the business problem and shaping the solution through to deployment, monitoring and continuous improvement.

Your work could include building a retrieval‑augmented generation solution that helps customer service agents access the right information, developing an agentic workflow that automates campaign planning and optimisation, product ionising recommendation and personalisation models, or creating customer intelligence solutions that power segmentation, propensity, next‑best‑action and decisioning capabilities.

You will work in multidisciplinary teams alongside data scientists, architects, engineers and client stakeholders. The problems you work on will often be ambiguous at the outset, requiring you to test assumptions, experiment quickly and iterate towards solutions that are scalable, reliable and ready for production.

This role requires strong technical curiosity and a builder mindset. You will be expected to stay current with developments in machine learning, generative AI and agentic systems, while applying sound engineering principles to create solutions that are secure, maintainable and effective in real‑world environments.

You will also work directly with clients, helping them understand how AI solutions work, what value they can create and the practical considerations involved in deploying them successfully. Whether building a personalisation model, deploying a customer service agent or creating a new customer decisioning capability, your focus will be on delivering measurable customer and business outcomes.

• Design, build and deploy AI‑powered tools, services and applications end to end, from problem definition through to live service and iteration.
• Develop generative AI solutions using prompt and context engineering, coding, retrieval‑augmented generation, fine‑tuning and applying evaluation techniques.
• Implement and optimise agentic AI workflows and multi‑step reasoning pipelines, integrating them with enterprise systems and data sources.
• Build, train, evaluate and deploy machine learning models, and define the approach for running and monitoring them in production.
• Break complex problems into smaller, testable components, and balance speed of experimentation with security, robustness and maintainability.
• Run…