Head of AI Solutioning
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Job Description
Lead AI Solutioning for clients, focusing on business processes and AI architecture development. Engage in technical leadership and team development within a remote work model.
Responsibilities:
- Analyze business processes and identify AI opportunities
- Design scalable AI architectures
- Develop end-to-end use cases—from PoC to production
- Define and own architectures for modern AI systems
- Establish best practices for MLOps / LLMOps
- Ensure scalability, security, and performance of AI solutions
- Integrate LLMs and ML models into existing enterprise landscapes
- Enable business units to effectively adopt and use AI
- Drive transformation and change initiatives
- Implement monitoring, drift detection, and retraining mechanisms
- Ensure reliable and efficient operation of production AI systems
- Build and grow an AI-focused team
- Support presales, proposals, and client presentations
Requirements:
- Several years of experience delivering AI/ML solutions in enterprise environments
- Strong consulting mindset with experience in client-facing roles (workshops, stakeholder management, C-level communication)
- Solid expertise in:
- Data Science & Machine Learning
- MLOps / LLMOps
- RAG architectures, agent systems, and modern AI patterns
- Monitoring, drift detection, and retraining strategies
- Strong programming skills in Python
- Hands-on experience with Docker, Kubernetes, MLflow
- Experience with at least one hyperscaler cloud: AWS, Azure, or GCP
- Proven ability to integrate AI into complex system landscapes (APIs, microservices, legacy systems)
- Strategic thinking combined with a hands-on mentality
- English
- German nice to have
Benefits:
- Consulting & Solution Design
- Analyze business processes and identify AI opportunities
- Design scalable AI architectures (especially LLM-based solutions)
- Develop end-to-end use cases—from PoC to production
- Technical Leadership
- Define and own architectures for modern AI systems (including RAG, agent-based systems, MCP approaches)
- Establish best practices for MLOps / LLMOps
- Ensure scalability, security, and performance of AI solutions
- Integration & Adoption
- Integrate LLMs and ML models into existing enterprise landscapes
- Enable business units to effectively adopt and use AI
- Drive transformation and change initiatives
- Operations & Quality
- Implement monitoring, drift detection, and retraining mechanisms
- Ensure reliable and efficient operation of production AI systems
- Address governance, compliance, and Responsible AI aspects
- Team & Business Development
- Build and grow an AI-focused team
- Support presales, proposals, and client presentations
- Act as a thought leader in Enterprise AI
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