Senior Infrastructure AI Specialist

pbThe role /b 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. /p h3Key Responsibilities /h3 ul liDefine and drive the AI/GenAI strategy for infrastructure services: AIOps, predictive operations, and intelligent automation. /li liArchitect and deliver production-grade MLOps and GenAIOps platforms on cloud (AWS, Azure, GCP). /li liDesign scalable AI pipelines for model training, evaluation, deployment, and lifecycle governance. /li liLead the implementation of GenAI-powered infrastructure use cases: incident prediction, capacity planning, anomaly detection, and self-healing automation. /li liArchitect RAG systems, LLM fine-tuning pipelines, and agentic AI workflows for infrastructure automation. /li liOversee GPU/TPU compute infrastructure provisioning and optimisation for AI/ML workloads. /li liImplement AI governance frameworks: model explainability, bias auditing, and regulatory compliance (EU AI Act). /li liDefine the MLOps tooling strategy: feature stores, experiment tracking, model registries, and inference serving platforms. /li liDefine and optimize AI FinOps and TokenOps practices, including model cost control, token consumption management, GPU utilisation, and chargeback/showback mechanisms. /li liEnsure AI security, data protection, model security, and secure integration with enterprise infrastructure platforms. /li liCollaborate with infrastructure, cloud, network, security, and operations teams to industrialize AI solutions within production environments. /li liAct as a technical advisor to client leadership on AI infrastructure integration and emerging AI trends. /li liMentor junior AI specialists and build internal AI capabilities across the infrastructure practice. /li liContribute to business development by shaping AI-enabled infrastructure propositions and use cases. /li /ul h3Who We Are /h3 pAccenture is a leading global professional services company that helps organizations build their digital core, optimize operations, accelerate growth and enhance services. We combine deep industry expertise with advanced technology capabilities in cloud, data AI, security and intelligent platforms. In Belgium and Luxembourg, our teams work with leading organizations across the public and private sectors to drive innovation, improve business performance and deliver meaningful outcomes through technology. Within the Intelligent Infrastructure Services practice, part of Digital Core Reinvention, you will join a team of specialists dedicated to transforming the technology foundations of our clients. Our scope spans cloud architecture and migration, AI applied to infrastructure, managed service excellence and digital workplace transformation. /p h3Your profile /h3 h3Language Requirements /h3 ul liFluency in English and fluency in French and/or Dutch is required. /li liAbility to work effectively in an international and multicultural environment. /li /ul h3Required Skills /h3 ul liDeep expertise in Python, ML frameworks (PyTorch, TensorFlow), and production ML engineering. /li liProven experience designing and operating MLOps / GenAIOps platforms at enterprise scale. /li liMastery of cloud AI/ML services: AWS SageMaker, Azure ML Studio, Google Vertex AI. /li liExpert knowledge of LLM architectures, RAG, fine-tuning (LoRA, QLoRA), and inference optimisation. /li liStrong background in agentic AI frameworks (LangChain, AutoGen, LlamaIndex, CrewAI). /li liExperience with vector databases (Pinecone, Weaviate, Chroma, pgvector) and semantic search. /li liDeep understanding of Kubernetes for AI workloads: GPU scheduling, distributed training, and inference serving. /li liStrong understanding of enterprise infrastructure operations, managed services, ITIL processes, and operational service delivery models. /li liExperience integrating AI solutions with enterprise tools such as ServiceNow, monitoring platforms, ITSM, observability, and automation ecosystems. /li liExperience with AI observability, prompt management, model monitoring, evaluation frameworks, and production incident management. /li liStrong knowledge of AI security, identity management, data governance, and secure AI deployment practices. /li liExperience managing token lifecycle, model consumption optimisation, and AI platform cost governance (TokenOps / AI FinOps). /li liExperience with Infrastructure as Code (Terraform, Ansible) and AI platform automation. /li liFamiliarity with AI evaluation frameworks, guardrails, model routing, and prompt engineerin