Company Description
We are SGS – the world's leading testing, inspection and certification company. Today, that mission is driven by data and technology. With 99,500 employees across 2,500 locations, we have a unique opportunity to build world-class digital products. We are now launching a new AI Hub in Madrid to do just that. This is your opportunity to build the foundational platform that will power our entire engineering organization.
Job Description
We are hiring a Full-Stack Engineer to build the core cloud platform that powers AI adoption across SGS. This is a hands-on engineering role where you will design and implement the foundational services, UIs, and tooling that allow AI engineers, software developers, and business teams to rapidly assemble solutions using reusable "Lego Bricks" (microservices, AI agents, data pipelines, and MLOps components).
You will work side-by-side with AI engineers, delivering microservices, internal tools, lightweight UIs, and developer-facing components that make AI delivery fast, compliant, and scalable.
We are at an early stage — this role is for someone who wants to move fast, learn fast, and build from scratch.
What You'll Build and Own
* Cloud Platform Foundations
o Build the first version of our internal developer platform using Go, lightweight HTML templates,and server-driven rendering.
o Design and implement core microservices (identity, data access, orchestration, monitoring, usage metering, etc.).
o Establish clean, composable service boundaries enabling AI teams to plug in new "bricks" easily.
* Web & UI Layer for AI Products and Tools
o Deliver pragmatic UIs using Go templates and pure HTML initially, then evolve toward a Go + Tailwind CSS + Templ + HTMX architecture.
- Build internal dashboards, admin tools, observability screens, and orchestration interfaces for AI agents and ML workflows.
* Developer Experience (DX) & Internal APIs
o Create APIs, CLIs, and utilities that enable AI engineers to deploy models, create agentic workflows, and integrate with shared components.
o Implement standards for API ergonomics, microservice contracts, and versioning.
* MLOps & AI Agent Enablement
o Collaborate with AI engineers to support model deployment, evaluation tooling, vector DB access, and agent orchestration.
o Provide the infrastructure primitives that allow the business teams to scale prototypes into production systems.
* Building for Speed and Evolution
o Ship fast, iterate fast: deliver valuable slices of functionality while the platform is still taking shape.
o Help shape the architectural blueprint for a scalable platform as the organization grows.
Qualifications
Required Technical Skills
* Strong engineering foundations with microservices, concurrency applying design patterns, 12 factor app principles and having a tidy first mindset.
* Solid background in REST APIs, event-driven services and distributed systems.
* Practical knowledge of cloud environments (AWS, GCP, or Azure).
* Experience with Docker, containerized development, and CI/CD pipelines.
* Understanding of MLOps concepts.
Bonus
* Familiarity / Some level of expertise with Go and/or Python.
* Experience with HTML, CSS, and server-side rendering frameworks (Go templates, etc.).
* Experience with microservice-first architectures.
* Exposure to LLM-powered applications, agent frameworks, or vector databases.
* Experience building internal developer tools or platforms.
* Familiarity with Kubernetes or serverless frameworks.
* Experience in early-stage, high-velocity environments.
Who You Are
* A pragmatic builder: You love shipping functional products quickly and evolving them with feedback.
* A full-stack problem solver comfortable moving across backend, frontend, and cloud.- A systems thinker who cares about architecture and platform foundations.
* A collaborator who enjoys working with AI engineers, product managers, and business partners.
* An owner who delivers reliable, scalable, maintainable systems.
Additional Information
What We Offer
* High impact from day one: Build the platform powering SGS's global AI strategy.
* Startup speed with enterprise scale and resources.
* Competitive compensation aligned with the strategic importance of the role.
* Opportunity to work on unique industrial and scientific datasets across materials science, supply chains, and sustainability.
* Growth paths into platform engineering, architecture, or technical leadership.