Product Engineer - DoiT Labs(Portugal)

Location
Our Product Engineer - DoiT Labs will be an integral part of our R&D team in EMEA. This role is based remotely as a full-time employee in the UK, Ireland, Estonia, the Netherlands, Sweden and Spain. We are also open to contractors in Eastern Europe and Portugal.

Who We Are
DoiT is a global technology company that works with cloud-driven organizations to leverage the cloud to drive business growth and innovation. We combine data, technology, and human expertise to ensure our customers operate in a well-architected and scalable state - from planning to production.
Delivering DoiT Cloud Intelligence, the only solution that integrates advanced technology with human intelligence, we help our customers solve complex multicloud problems and drive efficiency.
With decades of multicloud experience, we have specializations in Kubernetes, GenAI, CloudOps, and more. An award-winning strategic partner of AWS, Google Cloud, and Microsoft Azure, we work alongside more than 4,000 customers worldwide. 

The Mission
We’re hiring a Product Engineer to join DoiT Labs, our internal research department dedicated to building next-generation, AI-powered products that transform how organizations optimize cloud spend - across AI, Kubernetes, data clouds, and the broader multi-cloud landscape.

This role is for a builder who thinks end-to-end. You won’t just write code - you’ll deeply understand the problem, talk to customers, shape the solution, ship it fast, measure the impact, and iterate. We’re looking for someone with a “technical founder” mindset: equally comfortable digging into cloud billing data, prototyping a new AI-driven insight, or jumping on a call with a practitioner to pressure-test an idea.

AI-first is not a buzzword here - it’s how we work. You’ll be expected to use AI daily to amplify your own productivity and to embed intelligent capabilities into everything you build.

The Opportunity
As a Product Engineer in DoiT Labs, you will own the full lifecycle of solving meaningful problems for cloud practitioners - from discovery through delivery and beyond. This is not a role where someone else defines what to build and hands you a spec. You will be embedded in the problem space: understanding customer pain, identifying opportunities, designing solutions, building and shipping them rapidly, and learning from real-world usage to iterate.
Your work will focus on our next generation of cloud cost optimization products, spanning Generative AI, Kubernetes cost management, data cloud optimization, and broader multi-cloud financial intelligence. You’ll operate at the intersection of product thinking, full-stack engineering, and AI - leveraging large language models and intelligent automation to deliver step-change improvements in how practitioners manage cloud spend.

Responsibilities

• Full-lifecycle problem solving
• Own problems end-to-end: from understanding user pain, through solution design, implementation, release, measurement, and iteration - not just the coding step.
• Engage directly with customers and internal domain experts to build deep empathy for the workflows and challenges of cloud operators and FinOps practitioners.

Translate ambiguous problem spaces into clear, thin-sliced increments that can be shipped, measured, and learned from quickly.

AI-first product development

• Use AI tools daily to amplify your own engineering work - coding, analysis, research, and prototyping.
• Design and build AI-powered features as a default approach: intelligent recommendations, automated insights, natural-language interfaces, and predictive capabilities for cloud cost optimization.
• Make informed decisions on model selection, prompt engineering, latency/accuracy/cost tradeoffs, and responsible AI considerations as a core part of your engineering practice.

Fast iteration and shipping

• Operate with a bias toward action: prototype rapidly, ship frequently, and validate ideas through real customer usage rather than prolonged planning cycles.
• Build experiments and MVPs that generate measurable learning - and use those learnings to decide what to invest in next.
• Maintain high engineering standards without letting perfection slow down delivery; know when to take deliberate shortcuts and