AI Engineer

CompanyReward Gateway

LocationLondon, England, United Kingdom

Posted At3/6/2026

UK Visa Sponsorship Analytics

Analytics are greyed out due to low classification confidence (41.0%).

Occupation Type

Telecoms and related network installers and repairers

Occupation Code Skill LevelMedium Skilled

Sponsorship Salary Threshold

£41,700 (£21.38 per hour)

Standard minimum applies

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Description

Reward Gateway | Edenred, is a global leader in benefits and employee engagement. We help businesses attract, engage, and retain top talent through strategic rewards, recognition, and well-being solutions.

Guided by our shared missions of ‘Making the World a Better Place to Work’ and ‘Enriching Connections, For Good’, we are committed to transforming workplaces and improving people’s daily lives.

As we continue to expand our business, we have an opportunity for a hands-on AI Engineer who is excited about turning real-world challenges into smart, scalable solutions. You will work with the latest third‑party AI services, build streamlined workflows and craft high‑impact prompts that boost our internal tools, speed up developer productivity and elevate the customer experience. You will work closely with Product, Operations, and Engineering teams to turn ideas into practical solutions and contribute to improvements across the platform.

Key Responsibilities

Build and deliver production‑ready AI and Generative AI solutions using LLMs, RAG architectures, agents, and responsible AI practices.
Use AI coding assistants such as Cursor, GitHub Copilot, and Claude Code to accelerate development while maintaining ownership of outcomes and documenting best practices and repeatable patterns.

Manage cloud infrastructure and platform operations, including AWS, Kubernetes, CI/CD pipelines, Terraform, monitoring, performance optimisation, and cost control.

Design, develop, and maintain backend services in Python, and contribute to React, TypeScript, and PHP codebases when required.

Lead evaluation and iteration cycles, including defining and tracking offline and online metrics, running A/B tests, meeting latency and cost targets, implementing human‑in‑the‑loop validation, and ensuring robust observability.

Implement and maintain retrieval pipelines using embeddings, vector databases, hybrid search methods, and effective chunking strategies.

Collaborate closely with Product using a working‑backwards approach, producing technical designs, breaking down work, and delivering iteratively.

Improve internal AI development tooling, including shared libraries, SDKs, and reference implementations for RAG, tracing, prompt management, and evaluation.

Contribute to internal enablement and capability‑building activities across the organisation.

Partner with Security, Legal, and Data teams to define AI policies, review risks, and ensure privacy, PII protection, and regulatory compliance.

Mentor peers, conduct code reviews, and share knowledge to elevate engineering standards across the organisation.

Skills, Knowledge and Expertise

Proven experience in shipping production‑grade AI solutions.
Applied AI expertise across LLMs, RAG, agentic workflows, prompt engineering, embeddings, vector databases, hybrid search techniques, and effective chunking strategies.
Strong Python as a primary language, with solid testing practices and CI/CD experience; able to contribute when needed in React, TypeScript, and PHP or Node.js.
Cloud and platform engineering skills, including AWS, Kubernetes, Docker, infrastructure as code, and modern observability tooling.
Hands‑on experience with leading LLM providers such as Anthropic, Claude and OpenAI, with the ability to evaluate additional model providers and approaches.
Familiarity with LLM tooling ecosystems such as LangChain or LlamaIndex, agentic AI frameworks, vector stores, tracing and logging tools, prompt management platforms, and evaluation frameworks.
Strong data engineering capabilities, including dataset creation and validation, ETL development, SQL schema design, and the definition and tracking of meaningful product and model metrics.

Solid understanding of ML fundamentals and experimentation, including metric design, error analysis, model selection, and performance tuning.

A strong security and governance mindset, with the ability to communicate clearly with both technical and non‑technical audiences, and a high level of ownership from discovery through production and iterative improvement.

The Interview Process

Online interview with the Talent Partner and the Director of AI Engineering
Technical interview with Director of AI Engineering, VP of Product Engineering, and VP of Product

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.