AI Engineer

Reward Gateway

AI Engineer working on AI production systems at Reward Gateway | Edenred. Collaborating across teams to enhance developer productivity and customer experience.

Posted 7/3/2026full-timeLondon • 🇬🇧 United KingdomMid-LevelSenior💰 £80,000 - £90,000 per yearWebsite

Tech Stack

Tools & technologies

AWSCloudDockerETLKubernetesPythonTerraform

About the role

Key responsibilities & impact

Build and deliver production-ready AI and Generative AI solutions using LLMs, RAG architectures, and agents
Implement and maintain retrieval pipelines using embeddings, vector databases, hybrid search, and effective chunking strategies
Develop proof-of-concepts for emerging AI technologies and assess their production viability
Write clean, maintainable code following established engineering best practices and quality standards
Use AI coding assistants such as GitHub Copilot and Claude Code to accelerate development
Participate in code reviews and architectural discussions to improve code quality and system design
Work closely with Product and Engineering teams to understand requirements and deliver iterative solutions
Collaborate with cross-functional partners (Security, Data, Operations) to ensure solutions meet quality, compliance, and scalability requirements
Communicate technical decisions and tradeoffs clearly with both technical and non-technical stakeholders
Contribute to improving AI development practices and tooling through feedback and suggestions
Stay current with AI/ML developments, emerging frameworks, and best practices
Learn from more experienced engineers and contribute knowledge back to the team
Participate in capability-building activities and knowledge-sharing sessions
Build expertise in production AI systems, model evaluation, and optimization techniques

Requirements

What you’ll need

Solid software engineering experience with a focus on production Generative AI and RAG systems
Demonstrated experience building and deploying AI systems in production environments
Strong technical expertise in LLMs, RAG, prompt engineering, embeddings, and vector databases
Hands-on experience with leading LLM providers (Anthropic Claude, OpenAI, etc.)
Strong Python development skills and proficiency with AI coding assistants (Cursor, GitHub Copilot, Claude)
Production experience with AWS cloud services and familiarity with containerization (Docker, Kubernetes)
Solid understanding of ML fundamentals, model evaluation, and performance optimization
Data engineering capability, including working with datasets, ETL pipelines, and metrics definition
Nice to have (but not essential): Experience with agentic workflow systems and complex orchestration patterns
Background in NLP or contributions to open-source AI/ML projects
Experience with model fine-tuning or custom training approaches
Familiarity with MLOps platforms and experiment tracking tools
Experience with infrastructure as code (Terraform, CloudFormation)

Benefits

Comp & perks

At Reward Gateway | Edenred we are committed to ensuring an inclusive and accessible recruitment process for all candidates. If you have any specific requirements or need reasonable adjustments at any stage of the recruitment journey, please let your Talent Acquisition Partner know. Your needs are important to us, and we want to ensure an equitable experience for every candidate.
Be comfortable. Be you. We want every employee to feel comfortable bringing their passion, creativity, and individuality to work. We value all cultures, backgrounds, and experiences, because we believe diversity drives innovation and makes us stronger. Our approach to hiring and building teams is about more than filling roles - it’s about creating an environment where everyone can thrive, feel supported, and contribute to our mission of making the world a better place to work.

ATS Keywords

✓ Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

Production AI SystemsModel EvaluationPerformance OptimizationPrompt EngineeringEmbeddingsVector DatabasesETL PipelinesModel Fine-TuningInfrastructure as CodeContainerization

Soft Skills

CollaborationCommunicationCode ReviewKnowledge SharingFeedback Contribution