About Planet
Planet is a global provider of integrated technology and payments solutions for retail and hospitality customers.
We create great experiences for the millions of people who use our payments, software, and tax-free solutions every minute of every day.
Planet empowers its customers to deliver great customer experiences by combining payments and software in ways that drive greater loyalty, increase revenue and save time.
Founded over 35 years ago and with our headquarters in London, today we have more than 2,500 employees located across six continents serving our customers in more than 120 markets.
Role overview:
The AI Engineer owns the full lifecycle of AI-driven workflows across the organisation. This role is accountable not only for technical implementation but also for the quality, reliability, and outcomes of AI systems in production. You will design and operate end‑to‑end AI workflows that connect with internal systems, manage model behaviour, ensure decision accuracy, and continuously improve performance through measurement and iteration. This role requires a blend of software engineering excellence, AI systems understanding, and a strong product mindset focused on real‑world impact.
What you will doEnd-to-End AI Workflow Ownership
Design and build production‑grade AI workflows integrated across internal systems (HR, Finance, Ops, CRM, etc.).
Implement orchestration logic including triggers, retries, fallbacks, and human‑in‑the‑loop mechanisms.
Ensure workflows are reliable, observable, monitored, and auditable.
AI & Model Behaviour
Develop and maintain AI agents, prompts, retrieval pipelines, and decision logic.
Own and monitor model behaviour in production, including:
Accuracy and usefulness
Failure modes
Handling of edge cases and ambiguity
Improve model performance using evaluation frameworks, feedback loops, and real usage data.
Decision Quality & Outcomes
Take accountability for the quality of decisions produced by AI systems, beyond technical execution.
Define, track, and report success metrics (e.g., accuracy, resolution rate, time saved).
Diagnose situations where systems function technically but deliver suboptimal outcomes.
Collaboration & Platform Usage
Work closely with Data Engineering to leverage shared platforms:
Infrastructure
CI/CD
Data pipelines
Security & governance
Contribute to shared standards, schemas, and best practices for AI systems.
What Success Looks Like
AI workflows run reliably in production with minimal manual interventions.
Business users trust and rely on AI‑generated outputs.
Model behaviour improves consistently over time through structured evaluation.
Clear ownership and accountability when issues arise — no gaps.
Core Skills & Experience
Strong software engineering background (Python or similar).
Proven experience building production systems (not just prototypes).
Hands‑on experience with AI/ML systems (LLMs, classifiers, decision models, etc.).
Skilled in API integration and working with distributed systems.
AI & Data Expertise
Experience designing prompts, retrieval pipelines, or ML inference workflows.
Understanding of model evaluation, monitoring, and feedback loops.
Comfortable working with structured and unstructured data.
Mindset
Product‑oriented with a strong focus on outcomes, not just code delivery.
Comfortable navigating ambiguity and making pragmatic trade-offs.
Practical and grounded in building AI that works reliably in real-world production environments.
Nice to Have
Experience with workflow orchestration tools (e.g., Temporal, Airflow, Step Functions).
Experience developing internal tools or AI agents.
Familiarity with regulated or enterprise environments.
Exposure to MLOps or AI evaluation frameworks.
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.
