Senior Infrastructure AI Specialist is a AI Infrastructure Engineer role (full-time). with Accenture New Zealand. in BRUXELLES - BRUSSEL, BE. Imported listing (source: thejobsmap.com). Apply on the employer's site (thejobsmap.com).
Imported listing (source: thejobsmap.com) · Apply on thejobsmap.com
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Imported job description
Sourced from thejobsmap.com
Accenture is seeking a Senior Infrastructure AI Specialist to define and lead AI/GenAI strategy within infrastructure platforms. The role involves architecting scalable MLOps and GenAIOps frameworks, driving AI-powered observability and automation, and acting as a technical authority for AI integration across cloud infrastructure. The specialist will advise clients on AI-driven transformation roadmaps, contribute to business development, and mentor junior team members.
Responsibilities
Define and drive AI/GenAI strategy for infrastructure services including 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 implementation of GenAI-powered infrastructure use cases such as 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 including model explainability, bias auditing, and regulatory compliance (EU AI Act).
Define MLOps tooling strategy: feature stores, experiment tracking, model registries, and inference serving platforms.
Accenture New Zealand is the New Zealand practice of Accenture, a global professional-services and technology company. It helps organisations transform through strategy and consulting, technology, operations, industry-focused engineering, data and AI, cloud, and Song (marketing, sales and customer experience).
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.
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.
Contribute to business development by shaping AI-enabled infrastructure propositions.
Requirements
Fluency in English and French and/or Dutch.
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.
Excellent communication and stakeholder engagement skills, including CxO-level presentations.
Candidates must be citizens of a NATO member country and eligible to work in Belgium.
Nice to Have
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, ITIL 4 Foundation/Managing Professional.
Benefits
Health insurancelife insuranceretirement planpaid time offflexible scheduletraining budgetemployee stock purchase plancommuter benefitsdiscount program
Tech Stack
AIOpsAnsibleAutoGenAWS SageMakerAzure ML StudioChromaCrewAICUDADatadogDynatraceGenAIOpsGoogle Vertex AIKubernetesLangChainLlamaIndexLLMLoRAMLOpspgvectorPineconePythonPyTorchQLoRARAGServiceNowTensorFlowTerraformTritonWeaviate
