Customer AI Engineer

Overview

In this role you will design, build and deploy AI-powered solutions that deepen customer understanding and growth. You’ll work across the full lifecycle, from problem framing to production monitoring, in multidisciplinary teams. You’ll tackle ambiguous problems with experimentation and scalable, reliable AI products. You’ll stay current with generative and agentic AI while delivering measurable business value for clients.

Pay / Benefits

• Competitive salary
• Annual performance bonus
• Equity opportunities
• 25 days leave + volunteering days
• Flexible work policies
• Private healthcare and wellbeing support

Responsibilities

• Design, build and deploy AI-powered tools end-to-end from problem definition to live service
• Develop generative AI solutions using prompt engineering, retrieval-augmented generation, fine-tuning and evaluation
• Implement and optimise agentic AI workflows and multi-step reasoning pipelines integrated with enterprise data
• Build, train, evaluate and deploy ML models and define production monitoring approaches
• Break complex problems into testable components balancing speed, security and maintainability
• Run experiments to test new approaches and apply learnings to production-ready solutions
• Translate business problems into AI solutions with measurable value and clear stakeholder communication
• Document patterns and build reusable assets and contribute to capability building across the practice

Key requirements

• Production generative AI experience in delivering solutions to production environments
• Hands-on with LLM tooling (Claude, GPT or Gemini)
• Advanced Python and software engineering (production-grade code, OOP, asynchronous processing, packaging, testing)
• Generative AI and agentic development (prompt engineering, RAG, context management, embeddings, vector databases, LangChain/LangGraph/AutoGen)
• Customer domain experience in growth, personalization, marketing, commerce, sales or service
• ML foundations: training, evaluation, deployment, production monitoring
• Backend and cloud engineering (API/microservices, FastAPI; Azure/AWS/GCP)
• MLOps discipline (version control, CI/CD, model versioning, monitoring)
• Delivery track record from problem definition to deployed solution
• Strong communication and collaboration with stakeholders; fluent English

• Analytical and problem-solving
• Effective communicator to non-technical stakeholders
• Collaborative in cross-functional teams
• Claude, GPT, Gemini
• LangChain, LangGraph, AutoGen
• Retrieval-augmented generation

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