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.AI Infrastructure Principal Architect(m/w/x)
Designing and prototyping large-scale, cost-optimized AI/ML compute and training systems. Significant experience deploying AI/ML on-prem or cloud required. Hybrid work, 4-day work week.
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Anforderungen
- Significant experience coding, building, monitoring, troubleshooting AI/ML applications
- Significant experience selecting, designing AI/ML infrastructure
- Significant experience deploying AI/ML on premise or public cloud
- Strong understanding of AI and machine learning
- Strong understanding of computing infrastructure
- Preferred knowledge of AI infrastructure
- Well versed and proven experience in Python, Java, or C++
- Experience with data pipeline and workflow management tools
- Strong problem-solving skills
- Ability to work in a fast-paced environment
- Excellent communication skills
- Excellent collaboration skills
- Longstanding experience in AI/ML infrastructure engineering
- Longstanding experience in related roles on hyperscaler platform
- Longstanding experience deploying large scale AI/ML solutions
- Proven experience leading AI projects
- Proven experience managing AI projects
- Proven experience leading AI teams
- Proven experience managing AI teams
- Strong project management skills
- Ability to manage multiple projects simultaneously
- Demonstrated experience evaluating AI technologies and frameworks
- Demonstrated experience selecting AI technologies and frameworks
- Ability to work with cross-functional teams
- Ability to drive project alignment
Aufgaben
- Set technical vision for AI/ML compute infrastructure
- Establish direction for senior and lead architects
- Make authoritative architecture decisions for complex systems
- Prototype large-scale, cost-optimized compute and training systems
- Build reference implementations and proofs-of-concept
- Define scalable reference architectures and standards
- Implement foundational tooling and infrastructure-as-code
- Conduct enterprise-scale architecture assessments and design reviews
- Validate findings and profile real workloads
- Demonstrate optimization opportunities
- Shape AI infrastructure roadmap and technology strategy
- Evaluate emerging technologies and innovations
- Pilot promising technologies in real conditions
- Optimize computational stack performance and cost
- Profile GPU and compute utilization
- Tune distributed training and model-serving workloads
- Serve as principal authority on hyperscaler cloud platforms
- Troubleshoot and resolve complex issues across the stack
- Cultivate relationships with infrastructure partners
- Provide executive and client-level technical advisory
- Define monitoring and observability strategy
- Implement instrumentation, SLAs, and SLOs
- Ensure enterprise integration and compliance
- Mentor and develop senior and lead architects
- Champion cost-efficiency and value realization
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