AI Engineer

Location: London office - UK, Porto - Portugal, Warsaw - Poland, Madrid Office - Spain

Remote Type: Hybrid

Time Type: Full time

Job Description

About Us

Planet is a leading technology company transforming payments by putting customer experience first. We offer integrated solutions that include payment processing, VAT refunds, dynamic currency conversion, and management services for merchants in the Retail and Hospitality sectors worldwide.

In recent years, we have experienced significant growth, expanding our services and global presence.

With strong private equity investors, Advent International and Eurazeo, we have the financial capital and expertise to grow our capabilities and reach through acquisitions.

Our mission is to create a world of connected commerce where payments are simple, secure, and seamless, enabling our partners to deliver exceptional experiences to their customers.
 

Role Overview:

This role owns the design, delivery, and ongoing behaviour of production AI workflows that automate and augment work across Planet’s internal systems — HR, Finance, Operations, and CRM. You will be accountable not only for shipping the technology, but for the quality of the decisions it makes: building agents, prompts, and retrieval pipelines that business users can trust, and improving them over time through measurement and real-world feedback. The impact is direct — reliable, auditable AI that reduces manual effort and raises the quality and speed of everyday decisions across the business.
 

What you will do:

• Design and build production-grade AI workflows that integrate across internal systems (HR, Finance, Operations, CRM).

• Implement the orchestration logic — triggers, retries, fallbacks, and human-in-the-loop patterns — so workflows run reliably, observably, and auditably.

• Develop and maintain the AI agents, prompts, retrieval pipelines, and decision logic that power those workflows.

• Own model behaviour in production: accuracy and usefulness, failure modes, and edge-case and ambiguity handling.

• Iterate on model performance using evaluation frameworks, feedback loops, and real-world usage data.

• Take accountability for decision quality and outcomes — not just technical execution — defining and tracking success metrics such as accuracy, resolution rate, and time saved.

• Diagnose cases where systems are technically “working” but producing poor outcomes, and close the gap.

• Partner with Data Engineering on shared platforms (infrastructure, CI/CD, data pipelines, security and governance) and contribute to shared standards, schemas, and best practices for AI systems.

Who you are:

• Strong software engineering background (Python or similar), with a track record of building production systems — not just prototypes.

• Hands-on experience with AI / ML systems (LLMs, classifiers, decision models, or similar).

• Experience integrating APIs and working with distributed systems.

• Experience designing prompts, retrieval pipelines, or ML inference workflows.

• Solid understanding of model evaluation, monitoring, and feedback loops; comfortable working with both structured and unstructured data.

• Product-oriented — cares about outcomes, not just shipping code; pragmatic about AI and focused on what works in production.

• Comfortable owning ambiguity and making trade-offs.

• Languages: English

• A plus, but not required: workflow orchestration tools (Temporal, Airflow, Step Functions); experience building internal tools or agents; familiarity with regulated or enterprise environments; exposure to MLOps or AI evaluation frameworks.

Why Planet

Planet is an equal opportunity employer where diversity is valued, and all employment is decided based on qualifications, merit, and business need.

Come and grow your career in the most exciting, fast paced technology market, with a business that delivers feel-good connected commerce. We would love to hear from you – Apply now.

At Planet, we embrace a hybrid work model, with three days a week in the office.
 

Reasonable accommodations may be made in order to allow for an individual to perform the essential functions of this role successfully.