Senior Infrastructure AI Specialist

The role

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.

Key Responsibilities

• 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.

Who We Are

Accenture 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 and 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.

Your profile

Language Requirements

• Fluency in English and fluency in French and/or Dutch is required.
• Ability to work effectively in an international and multicultural environment.

Required Skills

• 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 Infra