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# AI Engineer

- **Company:** Planet
- **Location:** London, UK
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Application Integration Architecture, Continuous Integration, Information Engineering, Distributed Systems, Python (Programming Language), Software Engineering, Unstructured Data, Large Language Models, Model Validation, Build Management, Machine Learning Operations, Data Pipelines, Automation Anywhere
- **Published:** July 29, 2026
- **Apply:** https://uk.indeed.com/viewjob?jk=88ab5aaf84537a3f

## About the Role

* 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.

## Description

* 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.


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