Must have

• AI

• Microsoft Azure

• NLP

• Data pipelines

• Security

• GDPR

• Cloud

• Key Vault

• Degree

• Machine learning

• Azure Data Lake

• MLOps

• Python

• C#

• PyTorch

• TensorFlow

• scikit-learn

• Docker

• Kubernetes

• Communication skills

• Kanban

• Azure

• English (C1)

• Polish (B2)

Requirements description

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, 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.
• Deep hands-on experience with the Microsoft Azure ecosystem (e.g., Azure Machine Learning, Azure Cognitive Services, Azure OpenAI, Azure Synapse, Azure Data Lake).
• Proven track record in designing and deploying AI solutions at scale, including model development, data pipelines, and MLOps within Azure environments.
• Proficiency in programming languages such as Python or C#, and experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
• Strong knowledge of modern architectural patterns and cloud-native development, including containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines.
• Excellent analytical and communication skills; able to convey complex AI concepts to business and technical stakeholders alike.
• Experienced in Agile development environments and familiar with frameworks such as Scrum or Kanban.

Offer description

We are seeking an experienced AI Solution Architect to lead the design and delivery of a strategic, cross-functional content extraction and interpretation platform built on Microsoft Azure. This high-impact role will support multiple business units by delivering scalable, secure, and intelligent AI capabilities, leveraging Generative AI, NLP, and Azure-native services.

Your responsibilities

1. 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.
2. Collaborate with business stakeholders to translate requirements into scalable, production-grade AI architectures and deployment strategies.
3. Define and document robust AI solution architectures using Azure-native tools and services, including interfaces, data flows, and integration points.
4. Evaluate and recommend Azure-based AI technologies, frameworks, and services to meet performance, compliance, and scalability needs.
5. Design and implement secure, scalable data pipelines for training and inference of AI models, leveraging Azure AI services.
6. Work closely with technical teams to build, test, and operationalize AI models and algorithms aligned with best architectural practices.

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