AI Engineering Architect

Roboyo AI is a category shaper in Applied AI and Agentic Automation. We help leading enterprises move from automation projects and AI experiments into governed, production-grade systems that execute real work across workflows, processes, products and services. 

Our heritage is in scaled enterprise automation; our future is Applied AI and Agentic Automation: autonomous agents, AI engineering squads, process orchestration, data and knowledge foundations, and human-in-the-loop governance embedded deeply into how our clients operate. 

We are not here to sell AI theatre. We are here to turn AI into working systems, measurable outcomes and repeatable transformation programs. 

About the Role 

Across our practices, AI experimentation is organic: agents and accelerators get prototyped, they work, and they find their way to the teams who need them. Working is not the same as dependable, tested, hardened, properly deployed, and fit for a regulated client. Closing that distance and turning experimentation into an engineering discipline is the job.  

You will build and lead our AI engineering team: you, a Senior Engineer and a DevOps Engineer, and own the path from idea to production across every asset class. You will work with the SMEs within the practices to bring the prioritised pilots into products that deliver return.  
You are the final technical decision-maker on what ships. Roughly two-thirds of your week is hands-on: architecture, code, review. The rest is direction, hiring and the operating model. You will not be post-technical in this role. 

Responsibilities  

• Own the AI delivery operating model and the path from prototype to production, from intake, qualification, build, hardening to rollout across accelerators and client-facing IP.  

• Build and lead the AI engineering team: hire the two engineers, set the standards, review the code, unblock, grow – and work with SMEs across practices to design, build, deploy and maintain the apps.  

• Personally build, ship, harden agentic solutions, and own the assets you oversee. 

• Own architecture and production standards for all solutions: solution architecture, infrastructure, model selection, validation.  

• Turn governance into engineering: policy-as-code, guardrails, eval and release gates that run in the pipeline, controls our regulated clients would recognise, without slowing the build. 

• Embed AI capability across our practices: partner with automation, application engineering and consulting SMEs so their prototypes have a path to production.