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Listed on Accenture’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
Lead the design and implementation of AI and machine learning platforms embedded in infrastructure management systems, architecting production-grade MLOps and GenAIOps solutions across cloud environments. This role suits experienced ML engineers who want to move into infrastructure automation and advisory work, combining deep technical expertise with client-facing strategy and mentorship.
Our summary, not Accenture’s wording. The full posting is on their site.
Skills this role names
- Ansible
- AWS SageMaker
- Chroma
- CrewAI
- CUDA
- Datadog
- Dynatrace
- Google Vertex AI
- Kubernetes
- LangChain
- LlamaIndex
- Pinecone
- Python
- PyTorch
- Retrieval-Augmented Generation (RAG)
- ServiceNow
- TensorFlow
- Terraform
- Weaviate
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What they ask for
Required
- Deep expertise in Python and ML frameworks (PyTorch, TensorFlow)
- 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, and fine-tuning techniques
- Strong background with agentic AI frameworks (LangChain, AutoGen, LlamaIndex, CrewAI)
- Experience with vector databases (Pinecone, Weaviate, Chroma, pgvector)
- Deep understanding of Kubernetes for AI workloads
- Strong understanding of enterprise infrastructure operations and ITIL processes
- Experience integrating AI with enterprise tools (ServiceNow, ITSM, observability platforms)
- Experience with AI observability, prompt management, and model monitoring
- Strong knowledge of AI security, data governance, and secure AI deployment
- Experience managing token lifecycle and AI platform cost governance
- Experience with Infrastructure as Code (Terraform, Ansible)
- Fluency in English and French (or Dutch) required
- NATO member country citizenship
- Eligible to work in Belgium
Nice to have
- Experience with multimodal AI models and computer vision for infrastructure
- Knowledge of low-level optimisation (CUDA, Triton)
- Familiarity with AI-native observability platforms (Dynatrace Davis AI, Datadog Watchdog)
- Contribution to open-source AI or MLOps projects
- Background in edge AI or IoT sensor-driven ML
- Experience building AI agents integrated with enterprise platforms
- AWS Certified Machine Learning – Specialty
- Microsoft Certified Azure AI Engineer Associate or Data Scientist
- Google Professional Machine Learning Engineer
- NVIDIA Deep Learning Institute certifications
- Certified AI Governance Professional (CAIGP)
- ITIL 4 Foundation or Managing Professional