AI & DATA SCIENCE SPECIALIST (LIEGE-BELGIUM)

Gaming1; Luik; Sur place; EUR 90 000 - 120 000; Plein temps

Résumé du poste

Gaming1 in Liège, Belgium seeks an AI & Data Science Specialist to bridge AI engineering, governance, and production readiness. You will own AI application engineering, evaluation, and governance controls, partnering with Platform and Lab teams to deliver secure, observable, scalable AI solutions.

The role emphasizes responsible AI and turning experiments into reliable systems within a fast-moving product company, with hands-on technical depth and collaboration with data scientists.

Qualifications

• 5+ years of experience in software, ML or data engineering
• Strong Python skills and experience building maintainable AI applications
• Solid software engineering practices including design, testing, and API work
• Experience evaluating, deploying and monitoring AI systems with cost and latency awareness
• Ability to translate responsible AI and risk requirements into technical controls
• Professional proficiency in English
• Degree in Computer Science, AI/ML, Data Engineering or related field

Responsabilités

• Design and develop AI-powered systems from hypothesis to production use
• Establish evaluation and testing practices for quality, safety, and regressions
• Deploy, operate and monitor AI systems with observability and cost awareness
• Design safeguards like access controls and human oversight
• Build secure pipelines and knowledge systems for lab-wide use
• Partner with Data Scientists and Platform team to productionize experiments

Connaissances

Python

ML pipelines

Containerization

Kubernetes

AI governance

English proficiency

Formation

CS/AI degree

Outils

Docker

CI/CD

MLOps

Description du poste

We are looking for an AI & Data Science Speciliast with experience across AI engineering, governance and technical enablement to join the Experience Lab. Reporting to the AI Governance Lead and working closely with the Lab Co-Manager, you will be a senior hands-on technical contributor: building and deploying AI systems, helping teams apply AI governance in practice, and connecting the Lab's innovation work with the broader platform infrastructure.

This is neither a pure research role nor a platform engineering role. This role is for someone with strong technical depth, an understanding of responsible AI, and the ability to turn emerging capabilities into useful, reliable production systems within a fast-moving product company.

Your role sits at the intersection of exploration and execution. You will own AI application engineering, evaluation and technical governance controls, turning promising experiments into reliable systems that the Lab and the business can use with confidence. You will partner closely with the Platform team, which owns the shared production infrastructure, to ensure the Lab's AI solutions are secure, observable, scalable and production-ready.

YOUR ROLE

AI Engineering & Data Science

• Design and develop AI-powered systems — including models, agents and agentic workflows where appropriate — from hypothesis and experimentation through to reliable production use
• Establish systematic evaluation and testing practices covering quality, safety, failure modes and regressions, with clear criteria for deciding when an AI system is ready for production
• Deploy, operate and continuously improve AI systems, with appropriate observability, versioning and monitoring of reliability, latency, cost and model behavior
• Design safeguards such as access controls, auditability, human oversight and fallback mechanisms according to the risk and impact of each use case
• Build secure, reusable pipelines, context engineering frameworks and knowledge systems that the whole Lab can use and extend
• Partner with Data Scientists and the Platform team to turn promising experiments into maintainable, production-compatible services

AI Governance & Responsible AI

• Turn the AI Governance framework into practical engineering standards, reusable controls and clear delivery processes that teams can apply throughout the AI system lifecycle
• Help maintain an inventory of AI systems and support use-case intake, risk classification and technical assessment based on feasibility, value, impact and production readiness
• Define the evidence required for approval and ongoing operation — including system ownership, intended use, data and model dependencies, evaluation results, known limitations and human-oversight measures
• Implement and continuously improve technical controls for access, traceability, monitoring, human review, fallback behavior and incident escalation, proportionate to each system's risk
• Assess third-party AI models, tools and vendors from a technical perspective, including their reliability, security, privacy, transparency and operational constraints
• Partner with the AI Governance Lead, Legal, Data Protection, Security and Responsible Gaming teams to translate applicable requirements and internal policies into workable engineering controls
• Contribute technical guidance and examples to AI literacy initiatives, helping teams understand how to build and use AI systems responsibly

Cross-Functional Innovation

• Contribute to the Lab's cross-area intelligence capability — building tools and models that surface player signals, weak trends and hidden opportunities across journeys
• Develop value estimation models that project the business impact of AI-driven features before they reach the roadmap — turning exploration into investment decisions
• Collaborate with Data Scientists and the Platform team to turn promising prototypes into reliable, deployed solutions
• Build strong working relationships with the Platform team, ensuring that the Lab’s AI work is production-compatible and architecturally sound

YOUR ROLE

• Typically, 5+ years of experience in software, ML or data engineering, with demonstrated ownership of production systems and the ability to lead technical directions while remaining hands-on
• Strong Python skills and experience building maintainable AI applications, ML pipelines, agents or agentic workflows beyond the prototype stage
• Solid software engineering practices, including system design, automated testing, code review, API design, documentation and operational ownership
• Experience evaluating, deploying and monitoring AI systems, with an understanding of quality, safety, reliability, latency and cost trade-offs
• Hands-on experience with containerized and cloud-native delivery, including Docker, CI/CD pipelines, observability and common MLOps patterns. Familiarity with Kubernetes is a strong asset
• The ability to translate responsible AI, privacy, security and risk requirements into technical controls, documentation, evaluation evidence and review processes
• Professional proficiency in English
• A degree in Computer Science, AI/ML, Data Engineering or a related discipline, or equivalent practical experience
  

Nice to have:

• Experience with C# and the .NET ecosystem, particularly when integrating AI services with an existing .NET-based platform
• Experience with context engineering, retrieval-augmented generation, knowledge graphs, data modelling or other structured knowledge systems
• Experience applying AI governance or responsible AI practices in a regulated or data-sensitive environment
• Professional proficiency in French