Requirements

Expected technologies

Microsoft Azure

Docker

Kubernetes

Our requirements

• Education & experience: Bachelor’s or Master’s degree in Computer Science or a related field. 10+ years of experience as a Solutions Architect or Technical Lead, with a strong focus on enterprise AI and data-driven solutions.
• Area knowledge & experience: Deep hands-on experience with the Microsoft Azure ecosystem (e.g., Azure Machine Learning, Azure Cognitive Services, Azure OpenAI, Azure Synapse, Azure Data Lake, etc.). Proven track record in designing and deploying AI solutions at scale, including model development, data pipelines, and MLOps within Azure environments. Strong knowledge of modern architectural patterns and cloud-native development, including containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines.
• Soft & hard skills: Excellent analytical and communication skills; able to convey complex AI concepts to business and technical stakeholders alike
• International cooperation: Demonstrated experience in working collaboratively in geographically dispersed teams.

Your responsibilities

• Designing: Lead the end-to-end design and architecture of AI solutions — including models, data pipelines, and infrastructure — tailored to business needs and enterprise standards, with a focus on Microsoft Azure. Design and implement secure, scalable data pipelines for training and inference of AI models.
• Requirements management: Collaborate with business stakeholders to translate requirements into scalable, production-grade AI architectures and deployment strategies.
• Solution definition and documentation: Define and document robust AI solution architectures using Azure-native tools and services, including interfaces, data flows, and integration points.
• Solution evaluation, compliance & governance: Evaluate and recommend Azure-based AI technologies, frameworks, and services to meet performance, compliance, and scalability needs. Promote architectural governance and knowledge sharing across the AI delivery landscape.
• Cross-functional collaboration: Work closely with data scientists and ML engineers to build, test, and operationalize AI models and algorithms aligned with best architectural and MLOps practices. Provide hands-on technical leadership to multi-disciplinary project teams, mentoring architects and engineers across regions.
• Continuous improvement: Stay ahead of emerging Azure AI services and trends; identify opportunities for optimization and innovation across AI initiatives.