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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Infrastructure AI Specialist - **Company:** Accenture - **Location:** Brussel, Belgium - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Application Integration Architecture, Computer Vision, Microsoft Azure, Cloud Computing, Nvidia CUDA, Data Governance, Distributed Computing Environment, Monitoring of Systems, Identity and Access Management, Python (Programming Language), Machine Learning, Open Source Technology, Performance Tuning, Ansible, Tensorflow, Azure Machine Learning, Search Technologies, Software Deployment, AI Infrastructure, Datadog, Google Cloud, Azure Data Factory, Pytorch, Large Language Models, Prompt Engineering, IT Architecture, Deep Learning, Generative AI, AI Platforms, Kubernetes, Infrastructure Automation Frameworks, Enterprise Integration, Machine Learning Operations, Virtual Agents, Terraform, Dynatrace, Servicenow - **Published:** July 29, 2026 - **Apply:** https://be.indeed.com/viewjob?jk=ea6f214244b59d54 ## About the Role * Fluency in English and fluency in French and/or Dutch is required. * Ability to work effectively in an international and multicultural environment., * Deep expertise in Python, ML frameworks (PyTorch, TensorFlow), and production ML engineering. * Proven experience designing and operating MLOps / GenAIOps platforms at enterprise scale. * Mastery of cloud AI/ML services: AWS SageMaker, Azure ML Studio, Google Vertex AI. * Expert knowledge of LLM architectures, RAG, fine-tuning (LoRA, QLoRA), and inference optimisation. * Strong background in agentic AI frameworks (LangChain, AutoGen, LlamaIndex, CrewAI). * Experience with vector databases (Pinecone, Weaviate, Chroma, pgvector) and semantic search. * Deep understanding of Kubernetes for AI workloads: GPU scheduling, distributed training, and inference serving. * Strong understanding of enterprise infrastructure operations, managed services, ITIL processes, and operational service delivery models. * Experience integrating AI solutions with enterprise tools such as ServiceNow, monitoring platforms, ITSM, observability, and automation ecosystems. * Experience with AI observability, prompt management, model monitoring, evaluation frameworks, and production incident management. * Strong knowledge of AI security, identity management, data governance, and secure AI deployment practices. * Experience managing token lifecycle, model consumption optimisation, and AI platform cost governance (TokenOps / AI FinOps). * Experience with Infrastructure as Code (Terraform, Ansible) and AI platform automation. * Familiarity with AI evaluation frameworks, guardrails, model routing, and prompt engineering at scale. * Strong grasp of AI ethics, governance, responsible AI principles, and the implications of the EU AI Act. * Diplomatically bring the disruption of AI as a transformation enabler for the entire organization with smart communication, inspirational use cases and lead this by example. Demonstrate the AI Architecture mindset in challenges * Excellent communication and stakeholder engagement skills, including CxO-level presentations. * Fluency in French (mandatory) and professional-level English (mandatory). Preferred Skills * Experience with multimodal AI models and computer vision for infrastructure use cases. * Knowledge of low-level optimisation (CUDA, Triton) for inference acceleration. * Familiarity with AI-native observability platforms (Dynatrace Davis AI, Datadog Watchdog). * Experience contributing to open-source AI or MLOps projects. * Background in edge AI or IoT sensor-driven ML for infrastructure monitoring. * Experience building AI agents integrated with enterprise ecosystems and operational platforms. Relevant Certifications * AWS Certified Machine Learning - Specialty. * Microsoft Certified: Azure AI Engineer Associate (AI-102) or Azure Data Scientist (DP-100). * Google Professional Machine Learning Engineer. * NVIDIA Deep Learning Institute certifications. * Certified AI Governance Professional (CAIGP) or equivalent AI ethics certification. * Vendor-neutral Artificial Intelligence / Generative AI Professional certification * ITIL 4 Foundation / Managing Professional (for AI-enabled service operations environments) - (Preferred) Additional Requirements * Candidates must be citizens of a NATO member country. * Candidates must be eligible to work in Belgium. ## Description As a Senior Infrastructure AI Specialist, you will define the strategy and lead the design and delivery of AI and generative AI capabilities embedded within infrastructure platforms. You will architect scalable MLOps and GenAIOps frameworks, drive AI-powered observability and automation programmes, and act as the technical authority for AI integration across cloud infrastructure. You will advise clients on their AI-driven infrastructure transformation roadmaps, contribute to business development, and mentor junior specialists within the practice., * Define and drive the AI/GenAI strategy for infrastructure services: AIOps, predictive operations, and intelligent automation. * Architect and deliver production-grade MLOps and GenAIOps platforms on cloud (AWS, Azure, GCP). * Design scalable AI pipelines for model training, evaluation, deployment, and lifecycle governance. * Lead the implementation of GenAI-powered infrastructure use cases: incident prediction, capacity planning, anomaly detection, and self-healing automation. * Architect RAG systems, LLM fine-tuning pipelines, and agentic AI workflows for infrastructure automation. * Oversee GPU/TPU compute infrastructure provisioning and optimisation for AI/ML workloads. * Implement AI governance frameworks: model explainability, bias auditing, and regulatory compliance (EU AI Act). * Define the MLOps tooling strategy: feature stores, experiment tracking, model registries, and inference serving platforms. * Define and optimize AI FinOps and TokenOps practices, including model cost control, token consumption management, GPU utilisation, and chargeback/showback mechanisms. * Ensure AI security, data protection, model security, and secure integration with enterprise infrastructure platforms. * Collaborate with infrastructure, cloud, network, security, and operations teams to industrialize AI solutions within production environments. * Act as a technical advisor to client leadership on AI infrastructure integration and emerging AI trends. * Mentor junior AI specialists and build internal AI capabilities across the infrastructure practice. * Contribute to business development by shaping AI-enabled infrastructure propositions and use cases. ## Related Videos - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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