Senior AI Engineer (Agentic Systems)
Senior AI Engineer (Agentic Systems) UK Based Role At StarCompliance, we build software that
Overview
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 experienc
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 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
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)
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
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)
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 experienc
-
-
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)
-
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
-
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
-
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
AI & Agentic Engineering
Delivery & Problem Solving
Important Clarification
-
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
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