Senior Software Engineer
Role details
Job location
Tech stack
Job description
We are seeking a pragmatic, impact-driven AI-Native Senior Software Engineer to help evolve and extend our platform while guiding technical direction and supporting team development.
This role centres on solving real-world problems for art professionals, making thoughtful technical decisions that balance business impact with engineering trade-offs. You will work closely with Product Managers, Designers, and engineers to take ideas from inception through to production, ensuring we build the right solutions for our customers.
You will act as both a senior technical contributor and a product-minded engineer, someone who doesn't just implement requirements but actively shapes them. You will challenge assumptions, ask the right questions, and help ensure we are solving meaningful problems in the most effective way.
You will contribute to technical strategy by identifying opportunities to improve system reliability, performance, and user experience within our current architecture, while also supporting the evolution of our strategic platform.
This role will play a key part in driving the adoption of AI across engineering, leveraging emerging technologies and embedding AI-native workflows to improve how we design, build, and deliver software.
This role is predominantly hands-on, with around 20-30% of time on technical leadership: architecture, mentoring, and shaping how the team works., * Help the team move faster by adopting AI-native workflows and the shift to an agentic development lifecycle, without compromising the architectural qualities that make our platform reliable, scalable, and maintainable over the long term.
- Define guardrails, review practices, and validation gates that hold AI-generated code to our standards for security, reliability, and cost-efficiency (FinOps). These are first-class concerns in agentic pipelines, not afterthoughts.
- Design, build, and operate software across our platform, applying the same rigour to AI-augmented work as to anything else you ship.
- Embed deeply with Product and Design from the earliest stages of discovery, using rapid prototyping and iteration to compress the gap between idea and validated solution.
- Own complex features and ambiguous technical problems end-to-end, leveraging AI tooling to increase throughput without sacrificing engineering rigour.
- Serve as a critical technical voice, stress-testing AI-generated solutions, surfacing second-order risks, and ensuring the team builds the right thing, not just the fast thing.
- Shape platform architecture with an architectural lens, contributing to long-term decisions about how AI tooling, LLM integrations, and human-in-the-loop controls evolve across the stack.
- Continuously identify and exploit opportunities to improve performance, reliability, and user experience, using observability and analysis to find signals in noisy systems.
- Navigate confidently across legacy and greenfield contexts, applying AI tooling pragmatically to modernise where it matters most.
- Set the engineering standard, demonstrating through your own work what excellent looks like when much of the code is AI-generated.
- Grow the team's capability and confidence with AI-native practices, coaching more junior engineers to think critically about model outputs, prompt design, and the boundaries of automation.
- Define and evolve best practices for code quality, testing, documentation, and delivery in a world where much of the first draft is AI-generated.
- Evolve CI/CD pipelines to incorporate agentic workflows, automated testing, AI-assisted code review, and intelligent deployment gates.
- Champion secure by design principles across both human-written and AI-generated code, embedding security validation into the development pipeline rather than treating it as an afterthought.
- Own system observability and reliability, moving from reactive alerting to proactive, model-assisted incident prevention.
- Participate in on-call rotation, bringing the same rigour to incident response that you bring to day-to-day development., * Ask "what problem are we solving and why?" before reaching for a solution.
- Act as a genuine partner to Product and Design, shaping the problem, not just delivering against a spec.
- Balance technical debt and feature delivery with long-term business value in mind.
- Make confident decisions with incomplete information and thrive in fast-moving, evolving environments.
- Bring curiosity and hands-on experience with agentic workflows, using them to raise the bar for engineering..
Performance Indicators
- Delivery of high-quality, reliable solutions that drive measurable customer and business value.
- Meaningful contribution to the evolution of the strategic platform and overall technical direction.
- Improved technical decision-making, engineering standards, and team capability.
- Effective mentorship and development of engineers within the team.
- Strong partnership with Product and Design, resulting in well-shaped, high-impact solutions.
- Improvements in system performance, reliability, and maintainability.
- Consistent adoption of engineering best practices and processes.
Requirements
You're a hands-on senior engineer with a product mindset and the judgement to know which trade-offs matter. You care about business impact as much as engineering quality, and you've been building with AI tools long enough to know how to use them effectively., * Deep experience building scalable, secure, cloud-based systems, giving you the foundation to confidently guide and validate agentic workflow outputs.
- Proven ability to work across both legacy and greenfield codebases, using modern tooling to improve reliability and evolve architecture pragmatically.
- Strong system design fundamentals across scalability, performance, and distributed systems, including API design (REST, GraphQL).
- Hands-on experience with observability tooling (Datadog, Grafana, or similar) and a data-informed approach to system health and reliability.
- Solid SQL and data management skills, with an appreciation for AI-enabled, data-driven systems.
- Familiarity with CI/CD, containerisation (GitHub Actions, GitLab CI, Docker), and DevSecOps practices in modern AI development environments.
- A pragmatic approach to testing, knowing what to cover, what to skip, and how to use AI to improve efficiency.
- Experience mentoring engineers and supporting the adoption of modern tools and AI-native workflows.
- Strong communication skills and the ability to articulate technical decisions and trade-offs clearly.
Benefits & conditions
- Flexible working
- Premier private health insurance including dental, optical & EAP
- Childcare nursery benefit scheme
- Cycle to work scheme
- Tech buying scheme
- Free eye test
- Company pension scheme
- A focus on continued professional development including access to courses and training programs
