Why join us?
We’re a global tech company, just not the kind you’re picturing.
Sure, we’ve got catered lunches, team events, cool merch, and yes... dogs in the office. But that’s not why people join.
Our team of nearly a thousand people wakes up every day to make our product and our customers’ lives better. At SafetyCulture, you’ll hear “yes, let’s give it a shot” more often than “that’s not how we do things here.”
People join because we’re building tools that make work better for the 3 billion people who keep the world moving - factory floor operators, baggage handlers, truck drivers, servers, store assistants. The ones who make things happen. We’ve got the scale and innovation you’d expect from big tech. The difference? No endless layers of sign-off. No corporate theatre. Just smart, experienced people solving real problems fast .
The scale is big. But the ownership’s personal. Every full-time team member gets equity - real skin in the game. When we grow, you do too. We’re not perfect, no company is. But this next chapter of our growth is about scaling with intelligence, not just size - fueled by operational maturity, a clear vision, and a strong focus on AI.
This is big tech impact, without the big tech ick. If that excites you more than it scares you, you’ll fit right in.
Key responsibilities
Platform and infrastructure Own the AI Ops platform layer – containerised services, authentication layers, hosting, and deployment pipelines – so Business Unit engineers can build and ship reliably without depending on Platform Engineering for every component Build and maintain custom MCP (Model Context Protocol) connectors and integration components that multiple teams can rely on without duplicating effort – built to security and reliability standards from the start Implement CI/CD pipelines, automated testing, and deployment workflows for platform components Turn technical processes into easy-to-use skills for non-technical users, for example, building a governance-check skill that validates submissions before IT publishes them to the business Architecture and technical decisions Collaborate with the AI Ops Engineer to take conceptual architecture into detailed, implementable technical designs, then author Architecture Decision Records (ADRs) that document those decisions for new components and integration patterns Run Requests for Comment (RFCs) for cross-cutting proposals (e.g. skill governance and distribution): gathering input, driving consensus, and documenting outcomes Evaluate build-vs-buy decisions, factoring in maintenance burden, vendor lock-in, and platform alignment Governance and automation Co-own the technical implementation of skill governance with the broader AI Ops team: building and scaling the review, publish, and distribution pipeline so it handles company wide volume without becoming a bottleneck Progressively automate governance steps – replacing the highest-friction manual work first – so the review-to-publish pipeline scales without adding headcount Implement adoption tracking and usage instrumentation so the team can measure ROI, without necessarily owning the ongoing analysis Define and enforce platform standards: connector patterns, skill composition rules, metadata schemas, through tooling rather than process overhead Business Unit engineer guidance Act as the go-to peer consultant for Business Unit engineers – unblocking them on auth, hosting, and solution design before they build the wrong thing, and raising the quality of their builds over time Co-own application maturity with Business Unit engineers (the subject-matter experts for their domains): guiding them in the right way to take solutions from prototype through to production, and ensuring standards are met Create reusable patterns, templates, and reference implementations that lower the barrier for Business Unit engineers to build well, drive regular collaborative discussions with the Business Unit engineers to keep the community connected and encourage partnering on common requirements PDE community Bridge PDE best practices and tooling into the Business Unit context – so the AI Ops platform benefits from what engineering is already building, rather than reinventing it
Required Skills & Experience
Required skills & experience You have built and operated production systems end-to-end, not just contributed to them. You make sound architectural decisions without needing sign-off on every call, move fast without breaking things, and have the judgment that only comes from having shipped at pace and owned what breaks. This role won’t slow down while you find your feet. You’ll be expected to drive from day one. Technical Expert in at least one of Python, TypeScript, or Go, and comfortable picking up the others Strong understanding of containerisation (Docker as a minimum): able to design, build, and debug containerised services confidently Experience with authentication and authorisation systems: OAuth, service accounts