At a Glance
- Tasks: Lead the integration of AI into software development workflows and enhance engineering practices.
- Company: Join StarCompliance, a forward-thinking tech company focused on compliance solutions.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborative culture with a focus on innovation and ethical practices.
- Why this job: Make a real impact by embedding AI in engineering systems and transforming how software is built.
- Qualifications: Strong software engineering background with hands-on AI experience in cloud-based environments.
The predicted salary is between 70000 - 90000 £ per year.
UK Based Role. At StarCompliance, we build software that supports critical compliance needs for global clients. We are 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
- Autonomous or assisted code review
- AI-driven test generation and maintenance
- Code quality and compliance checks
Skills And Experience
- 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)
- 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
- CI/CD pipelines
- PR validation and automation
- Developer tooling
- Test generation and maintenance
- Code analysis, refactoring, and quality improvement
- Developer productivity at scale
- 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 teams.
Minimum Qualifications
- 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
Integrity and Ethics
All StarCompliance employees are expected to commit to a high standard of personal integrity and carry out their responsibilities in an ethical manner.
Senior AI Engineer (Agentic Systems) employer: StarCompliance
Contact Detail:
StarCompliance Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior AI Engineer (Agentic Systems)
✨Tip Number 1
Get your hands dirty! When you’re applying for a role like Senior AI Engineer, it’s crucial to showcase your practical experience. Dive into real codebases and demonstrate how you've embedded AI into development workflows. Show us what you can do!
✨Tip Number 2
Network like a pro! Connect with current employees or industry peers on platforms like LinkedIn. Ask them about their experiences at StarCompliance and share your passion for AI-assisted engineering. A personal touch can make all the difference!
✨Tip Number 3
Prepare for hands-on assessments! For a role that’s all about practical application, be ready to tackle coding challenges or design workflows during interviews. Brush up on your C# and CI/CD pipeline skills to impress us!
✨Tip Number 4
Apply through our website! We love seeing candidates who take the initiative. By applying directly, you’ll not only get noticed but also show your enthusiasm for joining our team. Let’s make AI a core capability together!
We think you need these skills to ace Senior AI Engineer (Agentic Systems)
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter to highlight your experience with AI in engineering workflows. We want to see how you've embedded AI into real systems, so don’t hold back on those details!
Showcase Your Hands-On Experience: We’re looking for practical examples of your work, especially with modern AI-native development environments like Cursor. Share specific projects where you’ve designed scalable AI workflows or integrated AI into CI/CD pipelines.
Be Clear and Concise: When writing your application, keep it straightforward. Use clear language to describe your skills and experiences, focusing on how they relate to the role. We appreciate a well-structured application that’s easy to read!
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it shows you’re keen to join our team!
How to prepare for a job interview at StarCompliance
✨Know Your AI Inside Out
Make sure you have a solid understanding of how AI can be embedded into development workflows. Be ready to discuss specific examples from your past experience where you've integrated AI into real engineering systems, not just as standalone tools.
✨Showcase Your Coding Skills
Since this role involves hands-on coding, brush up on your C# and .NET skills. Be prepared to demonstrate your coding abilities during the interview, possibly through a live coding exercise or by discussing previous projects where you’ve built scalable solutions.
✨Familiarise Yourself with CI/CD Pipelines
Understand how to integrate AI into CI/CD pipelines, especially using Azure DevOps. Be ready to explain how you've implemented automation triggers and hooks in your previous roles to enhance delivery lifecycles.
✨Prepare for Problem-Solving Scenarios
Expect to face complex problem-solving scenarios during the interview. Think about challenges you've encountered in past projects and how you overcame them, particularly in relation to improving engineering delivery or developer productivity.