Specialist Solution Architect, AI - Ecosystem
New
Based in ItalyFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- PythonArtificial IntelligenceCloud ComputingMachine LearningPyTorchData scienceTensorflowCI/CDMLOps
Requirements
- Hands-on experience in artificial intelligence, machine learning, data science, MLOps, LLMOps, or related domains.
- Practical experience developing AI applications (LLMs, RAG, NLP, deep learning, computer vision, or predictive analytics).
- Strong proficiency in Python or other statistical programming languages.
- Experience with leading open-source AI frameworks such as TensorFlow or PyTorch.
- Knowledge of AI system architecture, data pipelines, model training, and deployment.
- Familiarity with DevOps, CI/CD, Infrastructure as Code (IaC), and cloud-native environments.
- Strong communication and presentation skills.
- Experience leading workshops, architecture reviews, or customer-facing technical discussions.
- Ability to quickly learn new technologies and adapt to evolving AI trends.
- Strong collaboration and stakeholder management skills.
Responsibilities
- Deliver technical presentations, demonstrations, workshops, and solution deep dives to partners and stakeholders.
- Support pre-sales activities by contributing to technical proposals, architecture designs, RFIs, RFPs, and other solution-related documentation.
- Collaborate with partner solution architects, account teams, and technical stakeholders to identify challenges and accelerate AI initiatives.
- Design, architect, and guide the implementation of AI solutions, including machine learning pipelines, data architectures, model training environments, and deployment frameworks.
- Support proof-of-concept projects, technical evaluations, hackathons, and innovation initiatives.
- Drive ecosystem adoption by enabling partners to develop industry-specific AI use cases.
- Act as a trusted technical advisor, helping align AI strategies and technology roadmaps.
- Collaborate with product management and engineering to provide market feedback and contribute to product evolution.
- Create reusable technical assets, best-practice documentation, and enablement materials.
- Mentor less-experienced team members and participate in industry events and thought leadership activities.
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