AI Engineering Lead/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.
Requirements
- 8+ years in Engineering roles (software engineer, architect, platform or engineering lead)
- A production system you owned and defined (not contributed to). You defined its architecture, took it to production, and operated it – an e-commerce platform, ERP, a trading system, an internal product. You can walk us through the trade-offs you made and what you would do differently now.
- Player-coach track record. You have led engineers as their technical lead or manager, owned the architecture of systems they’ve built, while continuing to ship production code yourself. You shipped production code yourself in the last 12 months.
- Azure at expert level. Microsoft Certified: Azure Solutions Architect Expert (AZ-305), or 5+ years designing and running production Azure workloads, AKS or Container Apps, VNets, Entra ID, Key Vault, managed databases, infrastructure as code.
- You have delivered software governed by ISO 27001, SOC 2, DORA, HIPAA or equivalent, and you implemented the controls as running code: release gates, automated checks, policy-as-code, access control in the pipeline.
- At least one production system where Python was the primary language.
- AI-native by practice. You have built your own agents; have run open-weight models yourself (Ollama, llama.cpp, vLLM or similar), and AI coding agents are part of your workflow. Send us something you built – a repo, a write-up, a demo.
What would set you apart?
- Claude Certified Architect (Foundations or Professional)
