Customer AI Engineer in London

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.

The Team 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.

The Role 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, productionising 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.

What You Will Do 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 experiments to test new approaches and techniques, and apply what you learn to the solutions you are building. Translate business problems into AI solutions with clear and measurable value, and communicate technical concepts to client stakeholders and non‑technical audiences. Document patterns and build reusable assets and best practices that can be applied across the AI & Modelling Craft. Contribute to capability building and knowledge sharing across the Song AI & Data practice.

What’s In It For You Our Total Rewards consist of a competitive basic salary, annual performance bonus, opportunities to acquire equity and a wide range of health and wellbeing benefits. 25 days of leave per year plus 3 extra volunteering days for charitable work of your choice. Family‑friendly and flexible work policies. Attractive pension plan with financial wellbeing support and resources. Private healthcare insurance plan and Mental Wellbeing support. Employee Assistance Programme, Career Development and Counselling. A range of generous Parental Leave offerings.

What We Are Looking For Production generative AI experience. Experience delivering generative AI and LLM-based solutions into production environments, rather than experiments and proofs of concept alone. You have hands‑on experience with current LLM tooling such as Claude, GPT or Gemini. Advanced Python and software engineering. You write clean, production‑grade code rather than scripts, with a good working knowledge of object‑oriented design, asynchronous processing, packaging and automated testing. Generative AI and agentic development. Practical experience with prompt engineering, retrieval‑augmented generation pipelines, context management, embeddings and vector databases, and with agentic frameworks such as LangChain, LangGraph or AutoGen. Customer domain expertise. Experience applying AI to customer growth, personalisation, marketing, commerce, sales or service challenges. You can engage confidently in discussions about customer and business challenges and understand how AI creates measurable value for both customers and organisations. Machine learning foundations. Experience developing, training, evaluating and deploying machine learning models, and understanding when a traditional model is a better answer than an LLM. Backend and cloud engineering. API and microservices development using FastAPI or equivalent, and experience deploying and managing AI and machine learning workloads on Azure, AWS or GCP. Engineering discipline and MLOps. Version control, CI/CD pipelines, model versioning, and monitoring solutions once they are running in production. Delivery track record. Demonstrated end‑to‑end delivery, from problem definition through to a deployed and improved solution. You are comfortable working with incomplete requirements and can create clarity through the work itself. Communication and collaboration. Strong analytical and problem‑solving skills, the ability to explain technical concepts to senior client stakeholders, and fluency in English working in global, cross‑functional teams.

Preferred: Experience applying AI to customer, marketing, commerce, sales or service challenges. Preferred: Experience with natural language processing, computer vision or multimodal AI models. Preferred: Knowledge of Responsible AI, guardrails and safety practices in production environments. Preferred: Familiarity with Snowflake, Databricks or similar data platforms. Preferred: Consulting or client‑facing delivery experience. A bachelor’s or master’s degree in computer science, data science, engineering or a related field.