Staff Engineer

Valsea - Where Innovation Meets Entertainment
At Valsea, we're shaping the future of entertainment with immersive, engaging experiences that captivate players worldwide. Based in sunny Marbella, Spain, our vibrant open-space office is home to an international team of developers, marketers, product owners, and much more.
Valsea operates a leading scalable technology platform, 100% owned by us. This gives us the freedom to deliver results fast. We integrate selected outside companies into our ecosystem to ensure we provide fresh and vibrant products for our customers.
We work in a fast-paced environment, creating new brands, maintaining and improving our tech stack, launching leading acquisition and retention campaigns, and developing fresh features. Our ethos is simple - be best-in-class in everything we do.
Despite our rapid growth, Valsea still has that exciting start-up feel. We embrace a hybrid working model to balance professional growth with personal well-being. With a dynamic work environment, attractive salary, and a culture that thrives on collaboration, our power is our people.
We provide company laptops and other things you may need to work comfortably. Remote workers are always invited to our annual company trip and other team events. Ready to be part of something exciting?
What are we looking for now?
A Staff-level engineer who has done this before: someone who can walk into a mature, high-scale platform, understand where AI genuinely moves the needle versus where it's just noise, and lead the engineering work to get it done. You're comfortable being the most senior technical voice in the room, but you'd rather be pairing with an engineer on a gnarly integration than sitting in a strategy deck all day. You think in systems, not scripts: production reliability, data quality, and security aren't afterthoughts to you, they're the job.
This isn't a research role. It's a build role, with the freedom and seniority to also set direction.
What You'll Do
Own the technical strategy and roadmap for AI adoption across Valsea's product, platform, and internal tooling, grounded in what's actually feasible on our stack, not slideware.
Architect and lead the delivery of AI-powered capabilities into our Scala/Go/Kafka backend, from recommendation and personalisation to support/fraud/risk signals, agentic automation, and internal developer tooling.
Set the vision and roadmap for agentic QA automation (using AI agents to expand test coverage, catch regressions earlier, and reduce manual QA load) and for AI-accelerated support and incident response that cuts time-to-detect and time-to-resolve.
Introduce AI tooling at the root of our SDLC pipelines (GitHub/GitLab: code review, testing, deployment gates), so AI assistance is built into how the team ships, not bolted on afterward.
Evaluate and integrate LLM and ML infrastructure (self-hosted and API-based), making pragmatic build-vs-buy calls that respect our scale, latency, and cost constraints.
Set Product Development standards for how AI features get designed, tested, monitored, and rolled back. Model-driven systems should meet the same rigour as any other production service.
Partner directly with Engineering leadership, Product, and Data to identify where AI creates real player or business value, and turn that into a delivery plan.
Act as a technical multiplier: mentor senior and mid-level engineers and managers, run design reviews, and raise the technical bar for how the wider team thinks about AI-assisted and AI-driven systems.
Stay hands-on: prototype fast, get into the codebase, and unblock hard integration and performance problems yourself when it matters.
What You'll Bring
5+ years of software engineering experience, including significant time in the JVM/Scala ecosystem (or a comparably strong background in a statically-typed, distributed-systems-heavy language) plus proven Staff/Principal-level scope: setting technical direction, not just executing it.
Hands-on experience shipping AI/ML-powered features into production at real scale, with real users: LLM integration, recommendation systems, applied ML, or agentic infrastructure.
Deep comfort with event-driven, distributed architectures (Akka/Pekko, Kafka or equivalent); you understand the failure modes, not just the happy path.
Strong, hands-on experience with distributed systems and container orchestration: Kubernetes, Docker, and the operational patterns that come with running them at scale.
A track record of making sound build-vs-buy and platform decisions, including around cost, latency, data privacy, and vendor risk for AI systems specifically.
Product-minded: you think in terms of player impact and business outcomes, not just technical elegance.
A strong communicator who can move fluidly between a whiteboard architecture discussion with engineers and a plain-language case for leadership.
You