Want to know if this job is worth applying to?
Greater London, England, United Kingdom
Hybrid
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 Lead 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
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 guidance, examples, and training
Bootstrap new projects with AI-first engineering practices and tooling
Rapidly prototype and validate new approaches, focusing on real delivery impact
Ensure all AI-enabled workflows are robust, observable, and production-safe
Core Engineering
Strong software engineering background (ideally C# / .NET) in cloud-based SaaS environments
Experience building and operating distributed systems
Strong understanding of APIs, system design, and modern development practices
Experience with CI/CD pipelines (Azure DevOps preferred)
AI & Agentic Engineering
Hands-on experience using AI within real development workflows (not standalone tools)
Deep familiarity with AI-native IDEs (Cursor preferred, or similar)
Proven experience designing structured AI workflows, including:
Reusable prompts, skills, or templates
Multi-step or agent-based execution patterns
Tool integration and workflow orchestration
Experience integrating AI into engineering systems, such as:
CI/CD pipelines
PR validation and automation
Developer tooling
Practical application of AI to:
Test generation and maintenance
Code analysis, refactoring, and quality improvement
Developer productivity at scale
Delivery & Problem Solving
Track record of delivering production-grade solutions, not just prototypes
Experience enabling other engineers or teams to adopt new technologies at scale
Strong problem-solving skills in complex, evolving environments
Ability to define patterns where none exist and make them usable by others
Important Clarification
Experience limited to prompt-based tools used in isolation is not sufficient.
We are looking for engineers who have embedded AI into real engineering systems and workflows and have scaled those practices across team
Software engineering experience in cloud-based SaaS environments
Experience designing and evolving enterprise-scale distributed systems
Demonstrated impact in improving engineering delivery or developer productivity
Practical experience applying AI within professional engineering workflows
Experience working within enterprise SaaS platforms
Right to work in the country of employment
