Senior AI Engineer (Agentic Systems)

Senior AI Engineer (Agentic Systems) 

UK Based

Role 

At StarCompliance, we build software that supports critical compliance needs for global clients. We are now embedding AI as a core capability across the entire software development lifecycle. 

 

We are seeking a Senior AI Engineer to lead the practical adoption and scaling of AI-assisted and agentic engineering across our teams. 

 

This is not a research or experimentation role. You will work hands-on within real codebases, using modern AI-native development environments (Cursor preferred) to fundamentally change how software is built, tested, and delivered. Your focus is to turn AI from a tool into a system. Repeatable, scalable, and embedded. 

 

You will define and implement playbooks, patterns, and workflows that enable teams to operate with parallel AI agents, autonomous code review, and AI-driven delivery pipelines. You will also help bootstrap new initiatives, ensuring they start with the right architecture, tooling, and AI-enabled engineering practices from day one. 

 

This role sits within R&D Engineering and partners closely with Platform, QA, and Product Engineering. Influence is earned through delivery, not hierarchy.

 

How We Think About AI 

AI is not an assistant. It is part of the engineering system.  We expect engineers in this role to: 

• Embed AI directly into development workflows, not use it as a separate tool 

• Design repeatable, production-grade AI workflows, not one-off prompts 

• • Leverage agentic patterns such as multi-step execution, tool chaining, and parallelization 

  • Apply AI across the lifecycle: coding, testing, review, and delivery 

  • Balance speed with control, operating safely within a regulated SaaS environment 

  • Deliver measurable improvements in throughput, quality, and developer experience

Responsibilities

• Design and implement scalable AI-assisted engineering workflows across teams 

• Establish playbooks, standards, and best practices for agentic development 

• Build and operationalize: 

• Task-specific agents (e.g. test generation, refactoring, code analysis) 

• Reusable skills, templates, and workflows 

• Multi-agent and parallel execution patterns 

• Integrate AI into CI/CD pipelines (Azure DevOps preferred), including: 

• Autonomous or assisted code review 

• AI-driven test generation and maintenance 

• Code quality and compliance checks 

• Implement automation triggers and hooks to embed AI into the delivery lifecycle 

• Work directly within codebases to accelerate delivery and improve quality 

• Enable and upskill engineering teams through practical guida