Principal Architect, AI/ML
New
Fully remote work environment with flexibility for candidates based in the EU or United Kingdom.Full-TimePrincipal
Salary not disclosed
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Job Details
- Required Skills
- AWSGCPMachine LearningMicrosoft AzurePyTorchLLMMLOpsLangChain
Requirements
- Master’s degree in Computer Science, Mathematics, Engineering, Natural Sciences, or a related technical discipline, or equivalent practical experience.
- Extensive experience in senior or principal architecture roles.
- Deep expertise with at least one major cloud platform such as Google Cloud Platform, AWS, or Microsoft Azure.
- Proven expertise in large language model optimization techniques including quantization, pruning, LoRA fine-tuning, efficient inference, and model serving frameworks.
- Advanced knowledge of machine learning frameworks and high-performance training environments including PyTorch, JAX, or PyTorch/XLA.
- Experience designing and deploying agentic AI systems using frameworks such as LangGraph, LangChain, or Vertex AI Agent Builder.
- Strong understanding of AI observability, evaluation, governance, and monitoring frameworks.
- Expertise designing secure, private, and data-sovereign AI systems.
- Excellent communication, stakeholder management, and consulting skills.
- Demonstrated leadership, mentoring, and collaboration skills.
Responsibilities
- Lead the architecture, design, and delivery of sophisticated AI and machine learning solutions that address complex business and technical challenges across enterprise environments.
- Serve as a senior technical advisor during pre-sales engagements by helping scope opportunities, define solution strategies, and support technical presentations and client discussions.
- Design scalable, secure, and data-sovereign AI architectures aligned with regulatory requirements, privacy standards, ethical AI principles, and model explainability best practices.
- Architect and optimize AI platforms for hosting, fine-tuning, serving, and scaling both proprietary and open-source large language models across hyperscaler cloud environments.
- Guide engineering teams and clients on advanced AI/ML topics including high-performance model training, LLM optimization, inference efficiency, observability, and agentic workflows.
- Collaborate closely with cloud, infrastructure, and data architecture teams to ensure seamless integration of AI solutions into broader enterprise ecosystems.
- Lead the implementation of modern MLOps practices, deployment pipelines, monitoring frameworks, and AI governance standards for production-grade systems.
- Evaluate and recommend optimal cloud-native technologies, frameworks, and tooling to support scalable and cost-efficient AI deployments.
- Perform ROI analysis and cost optimization planning to ensure the long-term sustainability and operational efficiency of AI initiatives.
- Mentor engineering teams, promote technical excellence, and contribute to thought leadership through community engagement, technical publications, and industry presentations.
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