3 days ago
Kronberg, GermanyStaff+
Responsibilities
- Set the technical vision and strategy for compute infrastructure supporting large-scale AI/ML systems.
- Own complex architecture decisions across compute, networking, storage, orchestration, and model serving.
- Architect and hands-on prototype distributed training systems, reference implementations, proofs of concept, and benchmarks.
- Define reference architectures, standards, architectural patterns, infrastructure-as-code, and automation.
- Lead enterprise architecture assessments and design reviews, including workload profiling and optimization validation.
- Shape AI infrastructure roadmaps, capacity planning, scaling strategy, and technology evolution.
- Evaluate and pilot emerging AI infrastructure technologies in real-world conditions.
- Optimize GPU and compute utilization, distributed training, model serving, performance, and cost.
- Provide technical authority across hyperscaler cloud platforms, AI/ML services, accelerators, interconnects, and cost levers.
- Lead troubleshooting and root-cause analysis across hardware, networking, software, and models.
- Cultivate infrastructure partner relationships and represent the practice's technical credibility.
- Advise executives and clients on infrastructure trade-offs and business outcomes.
- Define and implement monitoring, observability, reliability, SLA, SLO, and cost/performance governance strategies.
- Ensure enterprise integration, security, compliance, and regulatory alignment.
- Mentor and develop senior and lead architects through pairing, design collaboration, and reviews.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, or a related engineering field.
- Significant experience coding, building, monitoring, and troubleshooting AI/ML model applications and designing infrastructure for on-premises or public-cloud deployment.
- Strong understanding of AI, machine learning, computing infrastructure, and preferably AI infrastructure.
- Expert proficiency in programming languages such as Python, Java, or C++.
- Experience with data pipeline and workflow management tools such as Apache Airflow and Kubeflow.
- Considerable AI/ML infrastructure engineering or related experience on a hyperscaler platform for deploying large-scale solutions.
- Proven experience leading and managing AI projects and teams.
- Experience evaluating and selecting AI technologies and frameworks.
- Strong project management, problem-solving, communication, collaboration, and cross-functional alignment skills.
Benefits
- Access to AI, cloud, and infrastructure certifications and continuous learning opportunities.
- Opportunity to shape enterprise-scale AI infrastructure strategy and technology roadmaps for global clients.
- Flexible working environment with significant autonomy, leadership impact, and long-term career growth opportunities.
Categories
About Accenture
Accenture is a global professional services firm providing management consulting, systems integration and technology, cybersecurity, and business process outsourcing for enterprises and governments. It operates a services-driven model delivering projects and managed services, often with major cloud and software partners, across industries worldwide. Headquartered in Dublin and publicly traded on the NYSE (ACN), it originated as Andersen Consulting and adopted the Accenture name in 2001.